Data: ChEDNT filename: chedamsLD1hindbrainsimple (SAP 12) 

Parameter: Hindbrain dams Lactation Day 1 

Model: R program – Single Sex 

BMR: 10% 

Power parameter set to 1 

Female Dose-Response Curve for Chlorpyrifos 
Thu Mar 26 09:16:05 2009 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 14.379 14.379 1.447 1.447 1.000 1.000 

mu 0.099 0.099 0.983 0.983 1.422 1.422 

lA.sexF 0.000 0.400 0.589 0.000 0.011 0.000 

lA.sexM 0.400 0.000 0.000 0.589 0.000 0.011 

lD.sexF 0.000 0.995 0.000 0.000 0.005 0.000 

lD.sexM 0.995 0.000 0.000 0.000 0.000 0.005 

lg.sexF 0.000 0.995 0.001 0.000 0.005 0.000 

lg.sexM 0.995 0.000 0.000 0.001 0.000 0.005 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.580 0.070 

lA.sexM 1.580 0.070 

lD.sexF 0.370 0.928 

lD.sexM 0.370 0.928 

lg.sexF 0.788 0.743 

lg.sexM 0.788 0.743 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.9126817 

lA.sexM 1.9126817 

lD.sexF 0.4933735 

lD.sexM 0.4933735 

lg.sexF 0.5612878 

lg.sexM 0.5612878 
Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.86148 (0.232147) 

BMD10 estimate (standard error): 1.451653 (1.674269) 

Power parameter estimate (standard error): 2.203495 (1.759592) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.4376629 4.861480 5.325774 

BMD 0.4390024 1.451653 4.800192 

Power Parameter 0.8484474 2.203495 5.722679 

 BMDL 

0.5366617 
Data: ChEDNT filename: chedamsLD1RBCsimple (SAP5) 

Parameter: RBC dams Lactation Day 1 

Model: R program – Single Sex 

BMR: 10% 

Power parameter set to 1 

Female Dose-Response Curve for Chlorpyrifos 

Wed Mar 25 09:22:59 2009 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 8.758 8.060 2.160 1.956 1.023 1.000 
mu 0.178 0.194 0.723 0.798 1.527 1.562 

lA.sexF 0.000 0.493 0.469 0.000 0.000 0.038 

lA.sexM 0.206 0.000 0.000 0.737 0.057 0.000 

lD.sexF 0.000 0.986 0.004 0.000 0.000 0.010 

lD.sexM 0.984 0.000 0.000 0.006 0.010 0.000 

lg.sexF 0.000 0.936 0.050 0.000 0.000 0.013 

lg.sexM 0.972 0.000 0.000 0.018 0.011 0.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.610 0.089 

lA.sexM 1.750 0.077 

lD.sexF -1.470 2.090 

lD.sexM -2.520 0.826 

lg.sexF -0.740 0.660 

lg.sexM -0.138 0.332 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 2.0816586 

lA.sexM 1.5309286 

lD.sexF 0.1692696 

lD.sexM 0.4306021 

lg.sexF 0.3497506 

lg.sexM 0.9885340 
 Variance is heterogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 5.099891 (0.2995380) 

BMD10 estimate (standard error): 0.05452521 (0.006826087) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.56378961 5.09989061 5.69896653 

BMD 0.04360681 0.05452521 0.06817738 

 BMDL 

0.0452703 

> 
Chlorpyrifos 91107 

Repeat oral (Corn Oil) 

Brain 

F 

(ADLT) 8 

time 

(hrs) 

BMDS Exponential MODEL variance 

Bmr = 10% 

!!! Warning: optimum may not have been found for Model 3 !!! 

!!! Bad completion code in maximum likelihood optimization routine !!! 

!!! Try choosing different initial values !!! 

Warning: optimum may not have been found form model 2 (BMDL). Bad process 

completion in Optimization routine. 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Aug 20 10:46:28 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 
 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 19.6141 19.6141 19.6141 

19.6141 

 rho -0.42086 -0.42086 -0.42086 -

0.42086 

 a 31357.8 366.014 54589.5 

54589.5 

 b 0.341823 -0.412747 0.329655 

0.315193 

 c -- -- 0.000294793 

0.280755 

 d -- 2 -- 

14 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 148.694 19.6141 148.694 

17.0871 

 rho -12.2946 -0.42086 -12.2946 -

0.175616 

 a 52527.4 366.014 52527.4 

51964.8 

 b 0.106739 -0.412747 0.106739 

0.622646 

 c -- -- 0 

0.309675 

 d -- 2 -- 

4.18895 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 5.198e+004 1803 

 0.05 8 5.193e+004 1031 

 0.1 8 5.199e+004 3291 

 0.5 8 5.169e+004 1862 

 1 8 4.736e+004 1938 

 3.5 8 1.609e+004 2362 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 5.253e+004 1865 -0.8333 

 0.05 5.225e+004 1927 -0.4678 
 0.1 5.197e+004 1992 0.02887 

 0.5 4.98e+004 2590 2.071 

 1 4.721e+004 3595 0.1163 

 3.5 3.615e+004 1.854e+004 -3.06 

 3 0 0 1.397e+069 1.053e-064 

 0.05 0 1.397e+069 1.052e-064 

 0.1 0 1.397e+069 1.053e-064 

 0.5 0 1.397e+069 1.047e-064 

 1 0 1.397e+069 9.591e-065 

 3.5 0 1.397e+069 3.259e-065 

 4 0 5.253e+004 1865 -0.8333 

 0.05 5.225e+004 1927 -0.4678 

 0.1 5.197e+004 1992 0.02887 

 0.5 4.98e+004 2590 2.071 

 1 4.721e+004 3595 0.1163 

 3.5 3.615e+004 1.854e+004 -3.06 

 5 0 5.196e+004 1979 0.01852 

 0.05 5.196e+004 1979 -0.05122 

 0.1 5.196e+004 1979 0.03642 

 0.5 5.17e+004 1979 -0.002227 

 1 4.736e+004 1995 -0.001818 

 3.5 1.609e+004 2193 0.0005003 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -389.2882 7 792.5764 

 A2 -383.9951 12 791.9902 

 A3 -389.2158 8 794.4316 

 R -479.6601 2 963.3202 

 2 -412.5291 4 833.0582 

 3 -2271.155 5 4552.31 

 4 -412.5291 4 833.0582 

 5 -389.2173 6 790.4345 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 
 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 191.3 10 < 0.0001 

 Test 2 10.59 5 0.06023 

 Test 3 10.44 4 0.03361 

 Test 4 46.63 4 < 0.0001 

 Test 5a 3764 3 < 0.0001 

 Test 5b -3717 1 N/A 

 Test 6a 46.63 4 < 0.0001 

 Test 6b -1.137e-013 0 N/A 

 Test 7a 0.002912 2 0.9985 

 Test 7b 3764 1 < 0.0001 

 Test 7c 46.62 2 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 
 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.987087 0.686322 

 3 -0.786421 Bad completion 

 4 0.987087 0.686322 

 5 1.03149 0.952164 

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 50000

 55000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

10:46 08/20 2010

BMDL BMD

 

Exponential
 0

 10000

 20000

 30000

 40000

 50000

 60000

-1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:46 08/20 2010

BMDL BMD

 

Exponential

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 50000

 55000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:46 08/20 2010

BMDL BMD

 

Exponential
 15000

 20000

 25000

 30000

 35000

 40000

 45000

 50000

 55000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

10:46 08/20 2010

BMDL BMD

 

Exponential
Chlorpyrifos 91107 

Repeat oral (Corn Oil) 

RBC 

M 

(PND) 8 

time 

(hrs) 

BMDS Exponential MODEL variance 

Bmr = 10% 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Aug 23 10:41:12 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 
 lnalpha 3.5618 3.5618 3.5618 

3.5618 

 rho 1.24098 1.24098 1.24098 

1.24098 

 a 1763.69 1763.69 6730.5 

6730.5 

 b 0.688802 0.688802 0.924147 

0.924147 

 c -- -- 0.040101 

0.040101 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 4.58344 4.58344 3.37202 

3.37202 

 rho 1.11897 1.11897 1.25812 

1.25812 

 a 5900.69 5900.69 6128.48 

6128.48 

 b 0.733957 0.733957 1.00583 

1.00583 

 c -- -- 0.0626966 

0.0626966 

 d -- 1 -- 

1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 6410 1605 

 0.05 8 5517 1180 

 0.1 8 5450 1294 

 0.5 8 4054 1112 

 1 8 2478 532.5 

 3.5 8 539.8 332.2 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 5901 1274 1.131 

 0.05 5688 1248 -0.3889 

 0.1 5483 1222 -0.0771 

 0.5 4088 1037 -0.09311 

 1 2832 844.7 -1.187 

 3.5 452.1 302.6 0.8195 

 3 0 5901 1274 1.131 

 0.05 5688 1248 -0.3889 

 0.1 5483 1222 -0.0771 

 0.5 4088 1037 -0.09311 

 1 2832 844.7 -1.187 

 3.5 452.1 302.6 0.8195 
 4 0 6128 1302 0.6115 

 0.05 5847 1264 -0.7388 

 0.1 5579 1227 -0.2973 

 0.5 3858 973.3 0.5691 

 1 2485 738.1 -0.02734 

 3.5 554.2 287.2 -0.1417 

 5 0 6128 1302 0.6115 

 0.05 5847 1264 -0.7388 

 0.1 5579 1227 -0.2973 

 0.5 3858 973.3 0.5691 

 1 2485 738.1 -0.02734 

 3.5 554.2 287.2 -0.1417 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -356.9792 7 727.9583 

 A2 -346.5243 12 717.0486 

 A3 -347.6798 8 711.3596 

 R -394.8545 2 793.709 

 2 -350.2632 4 708.5264 

 3 -350.2632 4 708.5264 

 4 -348.573 5 707.146 

 5 -348.573 5 707.146 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 
 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 96.66 10 < 0.0001 

 Test 2 20.91 5 0.0008425 

 Test 3 2.311 4 0.6788 

 Test 4 5.167 4 0.2706 

 Test 5a 5.167 4 0.2706 

 Test 5b 0 0 N/A 

 Test 6a 1.786 3 0.6179 

 Test 6b 3.38 1 0.06598 

 Test 7a 1.786 3 0.6179 

 Test 7b 3.38 1 0.06598 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 
 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.143551 0.119435 

 3 0.143551 0.119435 

 4 0.112166 0.0912869 

 5 0.112166 0.0912869 

 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

10:41 08/23 2010

BMDL BMD

 

Exponential
 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:41 08/23 2010

BMDL BMD

 

Exponential

 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:41 08/23 2010

BMDL BMD

 

Exponential
 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

10:41 08/23 2010

BMDL BMD

 

Exponential
Chlorpyrifos 91107 acute oral (Diet) Brain 

F 

(ADLT) 8 

time 

(hrs) 

BMDS Exponential MODEL variance 

Bmr = 10% 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Aug 20 09:01:08 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 143.444 143.444 143.444 

143.444 

 rho -11.8212 -11.8212 -11.8212 -

11.8212 
 a 48011.3 48011.3 56582 

56582 

 b 0.0263921 0.0263921 0.217692 

0.217692 

 c -- -- 0.692218 

0.692218 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 147.106 129.648 147.106 

131.002 

 rho -12.1486 -10.5465 -12.1486 -

10.6729 

 a 53554.6 53371.1 53554.6 

53338.2 

 b 0.0235669 0.0374083 0.0235669 

0.427433 

 c -- -- 0 

0.764479 

 d -- 1.32525 -- 

11.6354 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 5.389e+004 1242 

 0.05 8 5.27e+004 2265 

 0.1 8 5.302e+004 1698 

 0.5 8 5.384e+004 1737 

 2 8 5.129e+004 1639 

 10 8 4.113e+004 7650 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 5.355e+004 1658 0.5683 

 0.05 5.349e+004 1670 -1.341 

 0.1 5.343e+004 1681 -0.6866 

 0.5 5.293e+004 1781 1.457 

 2 5.109e+004 2207 0.2623 

 10 4.231e+004 6937 -0.4831 

 3 0 5.337e+004 1677 0.8712 

 0.05 5.336e+004 1679 -1.109 

 0.1 5.334e+004 1682 -0.5354 

 0.5 5.31e+004 1723 1.225 

 2 5.168e+004 1987 -0.5504 

 10 4.067e+004 7026 0.1819 

 4 0 5.355e+004 1658 0.5683 

 0.05 5.349e+004 1670 -1.341 

 0.1 5.343e+004 1681 -0.6866 

 0.5 5.293e+004 1781 1.457 
 2 5.109e+004 2207 0.2623 

 10 4.231e+004 6937 -0.4831 

 5 0 5.334e+004 1664 0.9338 

 0.05 5.334e+004 1664 -1.085 

 0.1 5.334e+004 1664 -0.5404 

 0.5 5.334e+004 1664 0.8603 

 2 5.147e+004 2013 -0.2437 

 10 4.078e+004 6975 0.1417 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -412.5905 7 839.181 

 A2 -389.5215 12 803.043 

 A3 -390.8265 8 797.653 

 R -437.9293 2 879.8586 

 2 -394.317 4 796.634 

 3 -393.454 5 796.9079 

 4 -394.317 4 796.634 

 5 -393.0053 6 798.0106 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 
 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 96.82 10 < 0.0001 

 Test 2 46.14 5 < 0.0001 

 Test 3 2.61 4 0.625 

 Test 4 6.981 4 0.1369 

 Test 5a 5.255 3 0.1541 

 Test 5b 1.726 1 0.1889 

 Test 6a 6.981 4 0.1369 

 Test 6b -2.274e-013 0 N/A 

 Test 7a 4.358 2 0.1132 

 Test 7b 0.8973 1 0.3435 

 Test 7c 2.623 2 0.2694 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 
 Model BMD BMDL 

 ------- ------------ ---------- 

 2 4.4707 3.3015 

 3 4.89291 3.61336 

 4 4.4707 3.27823 

 5 2.2233 2.07086 

 35000

 40000

 45000

 50000

 55000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:01 08/20 2010

BMDL BMD

 

Exponential
 35000

 40000

 45000

 50000

 55000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:01 08/20 2010

BMDL BMD

 

Exponential

 35000

 40000

 45000

 50000

 55000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:01 08/20 2010

BMDL BMD

 

Exponential
 35000

 40000

 45000

 50000

 55000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:01 08/20 2010

BMDL BMD

 

Exponential
Chlorpyrifos 91107 acute oral (Diet) RBC 

F 

(ADLT) 8 

time 

(hrs) 

BMDS Exponential MODEL variance 

Bmr = 10% 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Aug 20 08:36:34 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 6.5256 6.5256 6.5256 

6.5256 

 rho 0.685737 0.685737 0.685737 

0.685737 
 a 1255.83 1255.83 5648.48 

5648.48 

 b 0.350191 0.350191 0.374436 

0.374436 

 c -- -- 0.0059131 

0.0059131 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 5.70484 6.81983 5.67735 

6.1726 

 rho 0.823671 0.683928 0.826993 

0.726332 

 a 5529.09 5460.69 5531.07 

5291.21 

 b 0.354011 0.346782 0.355537 

0.456066 

 c -- -- 0.000761254 

0.0311856 

 d -- 1.09341 -- 

2.92331 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 5337 304.8 

 0.05 8 5164 575 

 0.1 8 5380 524.6 

 0.5 8 5219 575.3 

 2 8 2548 445.8 

 10 8 167 146 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 5529 602.8 -0.9014 

 0.05 5432 598.4 -1.267 

 0.1 5337 594 0.2034 

 0.5 4632 560.4 2.961 

 2 2724 450.3 -1.105 

 10 160.4 140.3 0.1331 

 3 0 5461 574 -0.6095 

 0.05 5396 571.7 -1.149 

 0.1 5324 569 0.2754 

 0.5 4713 545.8 2.62 

 2 2794 456.4 -1.523 

 10 111.1 151.5 1.044 

 4 0 5531 603.2 -0.91 

 0.05 5434 598.8 -1.274 

 0.1 5338 594.4 0.1974 

 0.5 4631 560.5 2.967 
 2 2719 449.7 -1.074 

 10 162.1 140.1 0.09854 

 5 0 5291 492.8 0.2629 

 0.05 5291 492.8 -0.7297 

 0.1 5291 492.7 0.5104 

 0.5 5224 490.5 -0.02753 

 2 2552 378.1 -0.03281 

 10 165 139.9 0.04026 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -314.7161 7 643.4321 

 A2 -306.9955 12 637.991 

 A3 -308.9629 8 633.9257 

 R -389.2592 2 782.5184 

 2 -316.5189 4 641.0378 

 3 -315.9325 5 641.865 

 4 -316.5177 5 643.0355 

 5 -309.3693 6 630.7387 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 
 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 164.5 10 < 0.0001 

 Test 2 15.44 5 0.008635 

 Test 3 3.935 4 0.4149 

 Test 4 15.11 4 0.004474 

 Test 5a 13.94 3 0.002989 

 Test 5b 1.173 1 0.2788 

 Test 6a 15.11 3 0.001725 

 Test 6b 0.00237 1 0.9612 

 Test 7a 0.8129 2 0.666 

 Test 7b 13.13 1 0.0002912 

 Test 7c 14.3 1 0.0001561 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 
 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.297619 0.266265 

 3 0.368228 0.274132 

 4 0.29658 0.25531 

 5 1.02712 0.598015 

 0

 1000

 2000

 3000

 4000

 5000

 6000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

08:36 08/20 2010

BMDLBMD

 

Exponential
 0

 1000

 2000

 3000

 4000

 5000

 6000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

08:36 08/20 2010

BMDL BMD

 

Exponential

 0

 1000

 2000

 3000

 4000

 5000

 6000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

08:36 08/20 2010

BMDLBMD

 

Exponential
 0

 1000

 2000

 3000

 4000

 5000

 6000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

08:36 08/20 2010

BMDL BMD

 

Exponential
mrid studytype sex 

time (hrs) mg/kg 

mean ChE 

ChE unit SD n species

48139301 acute oral (gavage) Brain 

F (ADLT) 8 0 54727.8 U/L 1283.2 8 RAT 

Exponential model Constant variance Bmr 10 Risk – relative  ====================================================================  Exponential Model. (Version: 1.7; Date: 12/10/2009)  Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d)  Gnuplot Plotting File:  Tue Jan 25 13:25:18 2011  ====================================================================  BMDS Model Run ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  The form of the response function by Model:  Model 2: Y[dose] = a * exp{sign * b * dose}  Model 3: Y[dose] = a * exp{sign * (b * dose)^d}  Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}]  Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]  Note: Y[dose] is the median response for exposure = dose;  sign = +1 for increasing trend in data;  sign = -1 for decreasing trend.  Model 2 is nested within Models 3 and 4. 
 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5.  Dependent variable = Mean  Independent variable = Dose  Data are assumed to be distributed: normally  Variance Model: exp(lnalpha +rho *ln(Y[dose]))  rho is set to 0.  A constant variance model is fit.  Total number of dose groups = 6  Total number of records with missing values = 0  Maximum number of iterations = 250  Relative Function Convergence has been set to: 1e-008  Parameter Convergence has been set to: 1e-008  MLE solution provided: Exact  Initial Parameter Values  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 16.9406 16.9406 16.9406 16.9406  rho(S) 0 0 0 0  a 38663.1 38663.1 57464.2 57464.2  b 0.0840269 0.0840269 0.0891764 0.0891764  c -- -- 0.000405057 0.000405057  d -- 1 -- 1  (S) = Specified  Parameter Estimates by Model  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 17.2111 17.0051 17.2111 17.0051  rho 0 0 0 0  a 54754.3 53165.5 54754.3 53165.5 
 b 0.077807 0.0920755 0.077807 0.0920755 

 c -- -- 0 7.90968e-024  d -- 2.31692 -- 2.31692  Table of Stats From Input Data  Dose N Obs Mean Obs Std Dev  ----- --- ---------- -------------  0 8 5.473e+004 1283  0.05 8 5.446e+004 2301  0.1 8 5.112e+004 2484  0.5 8 5.224e+004 1586  2 8 5.219e+004 2580  10 8 2.328e+004 1.157e+004  Estimated Values of Interest  Model Dose Est Mean Est Std Scaled Residual  ------- ------ ---------- --------- ----------------  2 0 5.475e+004 5462 -0.01372  0.05 5.454e+004 5462 -0.04158  0.1 5.433e+004 5462 -1.664  0.5 5.267e+004 5462 -0.2188  2 4.686e+004 5462 2.76  10 2.515e+004 5462 -0.9693  3 0 5.317e+004 4927 0.8968  0.05 5.317e+004 4927 0.744  0.1 5.316e+004 4927 -1.176  0.5 5.312e+004 4927 -0.5055  2 5.212e+004 4927 0.04146  10 2.328e+004 4927 -0.001059  4 0 5.475e+004 5462 -0.01372  0.05 5.454e+004 5462 -0.04158  0.1 5.433e+004 5462 -1.664  0.5 5.267e+004 5462 -0.2188  2 4.686e+004 5462 2.76  10 2.515e+004 5462 -0.9693  5 0 5.317e+004 4927 0.8968  0.05 5.317e+004 4927 0.744  0.1 5.316e+004 4927 -1.176 
 0.5 5.312e+004 4927 -0.5055 

 2 5.212e+004 4927 0.04146  10 2.328e+004 4927 -0.001059  Other models for which likelihoods are calculated:  Model A1: Yij = Mu(i) + e(ij)  Var{e(ij)} = Sigma^2  Model A2: Yij = Mu(i) + e(ij)  Var{e(ij)} = Sigma(i)^2  Model A3: Yij = Mu(i) + e(ij)  Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)  Model R: Yij = Mu + e(i)  Var{e(ij)} = Sigma^2  Likelihoods of Interest  Model Log(likelihood) DF AIC  ------- ----------------- ---- ------------  A1 -430.5745 7 875.1491  A2 -399.161 12 822.322  A3 -430.5745 7 875.1491  R -475.292 2 954.5839  2 -437.0672 3 880.1345  3 -432.1223 4 872.2447  4 -437.0672 3 880.1345  5 -432.1223 5 874.2447  Additive constant for all log-likelihoods = -44.11. This constant added to the  above values gives the log-likelihood including the term that does not  depend on the model parameters.  Explanation of Tests 
 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1)  Test 3: Are variances adequately modeled? (A2 vs. A3)  Test 4: Does Model 2 fit the data? (A3 vs. 2)  Test 5a: Does Model 3 fit the data? (A3 vs 3)  Test 5b: Is Model 3 better than Model 2? (3 vs. 2)  Test 6a: Does Model 4 fit the data? (A3 vs 4)  Test 6b: Is Model 4 better than Model 2? (4 vs. 2)  Test 7a: Does Model 5 fit the data? (A3 vs 5)  Test 7b: Is Model 5 better than Model 3? (5 vs. 3)  Test 7c: Is Model 5 better than Model 4? (5 vs. 4)  Tests of Interest  Test -2*log(Likelihood Ratio) D. F. p-value  -------- ------------------------ ------ --------------  Test 1 152.3 10 < 0.0001  Test 2 62.83 5 < 0.0001  Test 3 62.83 5 < 0.0001  Test 4 12.99 4 0.01135  Test 5a 3.096 3 0.3771  Test 5b 9.89 1 0.001662  Test 6a 12.99 4 0.01135  Test 6b -1.137e-013 0 N/A  Test 7a 3.096 2 0.2127  Test 7b -2.274e-013 1 N/A  Test 7c 9.89 2 0.00712  The p-value for Test 1 is less than .05. There appears to be a  difference between response and/or variances among the dose  levels, it seems appropriate to model the data.  The p-value for Test 2 is less than .1. Consider running  a non-homogeneous variance model.  The p-value for Test 3 is less than .1. You may want to  consider a different variance model. 
 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model.  The p-value for Test 5a is greater than .1. Model 3 seems  to adequately describe the data.  The p-value for Test 5b is less than .05. Model 3 appears  to fit the data better than Model 2.  The p-value for Test 6a is less than .1. Model 4 may not adequately  describe the data; you may want to consider another model.  Degrees of freedom for Test 6b are less than or equal to 0.  The Chi-Square test for fit is not valid.  The p-value for Test 7a is greater than .1. Model 5 seems  to adequately describe the data.  The p-value for Test 7b is less than .05. Model 5 appears  to fit the data better than Model 3.  The p-value for Test 7c is less than .05. Model 5 appears  to fit the data better than Model 4.  Benchmark Dose Computations:  Specified Effect = 0.100000  Risk Type = Relative deviation  Confidence Level = 0.950000  BMD and BMDL by Model  Model BMD BMDL  ------- ------------ ----------  2 1.35413 1.15758  3 4.11184 2.26115  4 1.35413 1.13779 
 5 4.11184 2.26115 
 10000

 20000 30000 40000 50000 60000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

13:25 01/25 2011

BMDL BMD

Exponential
 10000

 20000 30000 40000 50000 60000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

13:25 01/25 2011

BMDL BMD

Exponential
 10000

 20000 30000 40000 50000 60000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

13:25 01/25 2011

BMDL BMD

Exponential
 10000

 20000 30000 40000 50000 60000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

13:25 01/25 2011

BMDL BMD

Exponential

48139301 acute oral (gavage) RBC 

F (ADLT) 8 0 5409.3 U/L 350.5 8 RAT 

Exponential model Constant variance Bmr 10 Risk – relative  ====================================================================  Exponential Model. (Version: 1.7; Date: 12/10/2009)  Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d)  Gnuplot Plotting File:  Tue Jan 25 14:08:23 2011  ====================================================================  BMDS Model Run ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  The form of the response function by Model:  Model 2: Y[dose] = a * exp{sign * b * dose}  Model 3: Y[dose] = a * exp{sign * (b * dose)^d}  Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}]  Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]  Note: Y[dose] is the median response for exposure = dose;  sign = +1 for increasing trend in data;  sign = -1 for decreasing trend.  Model 2 is nested within Models 3 and 4.  Model 3 is nested within Model 5.  Model 4 is nested within Model 5. 
 Dependent variable = Mean 

 Independent variable = Dose  Data are assumed to be distributed: normally  Variance Model: exp(lnalpha +rho *ln(Y[dose]))  rho is set to 0.  A constant variance model is fit.  Total number of dose groups = 6  Total number of records with missing values = 0  Maximum number of iterations = 250  Relative Function Convergence has been set to: 1e-008  Parameter Convergence has been set to: 1e-008  MLE solution provided: Exact  Initial Parameter Values  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 12.8192 12.8192 12.8192 12.8192  rho(S) 0 0 0 0  a 2602.7 2602.7 6273.23 6273.23  b 0.188905 0.188905 0.199083 0.199083  c -- -- 0.000135704 0.000135704  d -- 1 -- 1  (S) = Specified  Parameter Estimates by Model  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 13.1907 13.0394 13.1907 12.9692  rho 0 0 0 0  a 5659.79 5508.33 5659.79 5505.95  b 0.157864 0.158148 0.157864 0.455332  c -- -- 0 0.154615 
 d -- 1.42637 -- 13.5011 

 Table of Stats From Input Data  Dose N Obs Mean Obs Std Dev  ----- --- ---------- -------------  0 8 5409 350.5  0.05 8 5146 533.3  0.1 8 5494 356.9  0.5 8 5975 1150  2 8 4360 536.2  10 8 851.3 622.2  Estimated Values of Interest  Model Dose Est Mean Est Std Scaled Residual  ------- ------ ---------- --------- ----------------  2 0 5660 731.7 -0.9683  0.05 5615 731.7 -1.814  0.1 5571 731.7 -0.2982  0.5 5230 731.7 2.877  2 4127 731.7 0.8982  10 1167 731.7 -1.222  3 0 5508 678.4 -0.4129  0.05 5503 678.4 -1.488  0.1 5493 678.4 0.002137  0.5 5363 678.4 2.551  2 4539 678.4 -0.746  10 805.3 678.4 0.1918  4 0 5660 731.7 -0.9683  0.05 5615 731.7 -1.814  0.1 5571 731.7 -0.2982  0.5 5230 731.7 2.877  2 4127 731.7 0.8982  10 1167 731.7 -1.222  5 0 5506 655 -0.4174  0.05 5506 655 -1.554  0.1 5506 655 -0.0516  0.5 5506 655 2.023  2 4360 655 -1.746e-007 
 10 851.3 655 3.65e-008 

 Other models for which likelihoods are calculated:  Model A1: Yij = Mu(i) + e(ij)  Var{e(ij)} = Sigma^2  Model A2: Yij = Mu(i) + e(ij)  Var{e(ij)} = Sigma(i)^2  Model A3: Yij = Mu(i) + e(ij)  Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)  Model R: Yij = Mu + e(i)  Var{e(ij)} = Sigma^2  Likelihoods of Interest  Model Log(likelihood) DF AIC  ------- ----------------- ---- ------------  A1 -331.6616 7 677.3232  A2 -323.0454 12 670.0909  A3 -331.6616 7 677.3232  R -384.4019 2 772.8038  2 -340.5775 3 687.1549  3 -336.9452 4 681.8904  4 -340.5775 3 687.1549  5 -335.262 5 680.524  Additive constant for all log-likelihoods = -44.11. This constant added to the  above values gives the log-likelihood including the term that does not  depend on the model parameters.  Explanation of Tests  Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R)  Test 2: Are Variances Homogeneous? (A2 vs. A1) 
 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2)  Test 5a: Does Model 3 fit the data? (A3 vs 3)  Test 5b: Is Model 3 better than Model 2? (3 vs. 2)  Test 6a: Does Model 4 fit the data? (A3 vs 4)  Test 6b: Is Model 4 better than Model 2? (4 vs. 2)  Test 7a: Does Model 5 fit the data? (A3 vs 5)  Test 7b: Is Model 5 better than Model 3? (5 vs. 3)  Test 7c: Is Model 5 better than Model 4? (5 vs. 4)  Tests of Interest  Test -2*log(Likelihood Ratio) D. F. p-value  -------- ------------------------ ------ --------------  Test 1 122.7 10 < 0.0001  Test 2 17.23 5 0.00408  Test 3 17.23 5 0.00408  Test 4 17.83 4 0.001331  Test 5a 10.57 3 0.01431  Test 5b 7.265 1 0.007033  Test 6a 17.83 4 0.001331  Test 6b 1.137e-013 0 N/A  Test 7a 7.201 2 0.02731  Test 7b 3.366 1 0.06654  Test 7c 10.63 2 0.004915  The p-value for Test 1 is less than .05. There appears to be a  difference between response and/or variances among the dose  levels, it seems appropriate to model the data.  The p-value for Test 2 is less than .1. Consider running  a non-homogeneous variance model.  The p-value for Test 3 is less than .1. You may want to  consider a different variance model.  The p-value for Test 4 is less than .1. Model 2 may not adequately 
 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately  describe the data; you may want to consider another model.  The p-value for Test 5b is less than .05. Model 3 appears  to fit the data better than Model 2.  The p-value for Test 6a is less than .1. Model 4 may not adequately  describe the data; you may want to consider another model.  Degrees of freedom for Test 6b are less than or equal to 0.  The Chi-Square test for fit is not valid.  The p-value for Test 7a is less than .1. Model 5 may not adequately  describe the data; you may want to consider another model.  The p-value for Test 7b is greater than .05. Model 5 does  not seem to fit the data better than Model 3.  The p-value for Test 7c is less than .05. Model 5 appears  to fit the data better than Model 4.  Benchmark Dose Computations:  Specified Effect = 0.100000  Risk Type = Relative deviation  Confidence Level = 0.950000  BMD and BMDL by Model  Model BMD BMDL  ------- ------------ ----------  2 0.667414 0.553107  3 1.30544 0.861254  4 0.667414 0.552815  5 1.88369 1.16012 
 0

 1000 2000 3000 4000 5000 6000 7000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

14:08 01/25 2011

BMDL BMD

Exponential
 0

 1000 2000 3000 4000 5000 6000 7000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

14:08 01/25 2011

BMDL BMD

Exponential
 0

 1000 2000 3000 4000 5000 6000 7000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

14:08 01/25 2011

BMDL BMD

Exponential
 0

 1000 2000 3000 4000 5000 6000 7000

 0 2 4 6 8 10

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

14:08 01/25 2011

BMDL BMD

Exponential

91107 acute oral (gavage) brain F (PND) 8

Exponential model 

Constant variance 

Bmr 10 

Risk – relative 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 07 15:07:15 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 
 -------- ------- ------- ------- --

----- 

 lnalpha 15.7893 15.7893 15.7893 

15.7893 

 rho(S) 0 0 0 

0 

 a 17507.3 17507.3 26641 

26641 

 b 0.160447 0.160447 0.163349 

0.163349 

 c -- -- 0.000411223 

0.000411223 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 16.149 15.7974 16.149 

15.7962 

 rho 0 0 0 

0 

 a 25897.2 25018.4 25897.2 

25009.1 

 b 0.136582 0.185163 0.136582 

0.42834 

 c -- -- 0 

0.438057 

 d -- 2.4765 -- 

11.8526 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 2.463e+004 3768 

 0.05 8 2.512e+004 2987 

 0.1 8 2.491e+004 3080 

 0.5 8 2.537e+004 2069 

 2 8 2.293e+004 3188 

 5 8 1.096e+004 1517 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2.59e+004 3212 -1.117 

 0.05 2.572e+004 3212 -0.525 

 0.1 2.555e+004 3212 -0.5596 

 0.5 2.419e+004 3212 1.043 

 2 1.971e+004 3212 2.841 

 5 1.308e+004 3212 -1.873 

 3 0 2.502e+004 2694 -0.4091 

 0.05 2.502e+004 2694 0.1119 
 0.1 2.502e+004 2694 -0.1121 

 0.5 2.495e+004 2694 0.444 

 2 2.297e+004 2694 -0.03888 

 5 1.095e+004 2694 0.005068 

 4 0 2.59e+004 3212 -1.117 

 0.05 2.572e+004 3212 -0.525 

 0.1 2.555e+004 3212 -0.5596 

 0.5 2.419e+004 3212 1.043 

 2 1.971e+004 3212 2.841 

 5 1.308e+004 3212 -1.873 

 5 0 2.501e+004 2692 -0.3996 

 0.05 2.501e+004 2692 0.1216 

 0.1 2.501e+004 2692 -0.1037 

 0.5 2.501e+004 2692 0.3817 

 2 2.293e+004 2692 -3.368e-008 

 5 1.096e+004 2692 9.54e-009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -402.9422 7 819.8844 

 A2 -399.1602 12 822.3204 

 A3 -402.9422 7 819.8844 

 R -439.9613 2 883.9227 

 2 -411.577 3 829.154 

 3 -403.1386 4 814.2772 

 4 -411.577 3 829.154 

 5 -403.1082 5 816.2164 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 
 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 81.6 10 < 0.0001 

 Test 2 7.564 5 0.182 

 Test 3 7.564 5 0.182 

 Test 4 17.27 4 0.001713 

 Test 5a 0.3927 3 0.9417 

 Test 5b 16.88 1 < 0.0001 

 Test 6a 17.27 4 0.001713 

 Test 6b 0 0 N/A 

 Test 7a 0.332 2 0.847 

 Test 7b 0.06072 1 0.8054 

 Test 7c 16.94 2 0.0002099 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 
 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.771408 0.641655 

 3 2.17675 1.5596 

 4 0.771408 0.641603 

 5 2.03466 1.56599 

 10000

 15000

 20000

 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

15:07 09/07 2010

BMDL BMD

 

Exponential
 10000

 15000

 20000

 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

15:07 09/07 2010

BMDL BMD

 

Exponential

 10000

 15000

 20000

 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

15:07 09/07 2010

BMDL BMD

 

Exponential
 10000

 15000

 20000

 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

15:07 09/07 2010

BMDL BMD

 

Exponential
Chlorpyrifos 

MRID 48139301 

Acute Oral – Gavage 

Female PND 11 Brain ChE 

R-based single sex program

Fri Jan 21 09:52:10 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 9.633 9.633 1.512 1.512 1.000 1.000 

mu 0.149 0.149 0.952 0.952 1.439 1.439 

lA.sexF 0.204 0.000 0.000 0.765 0.031 0.000 

lA.sexM 0.000 0.204 0.765 0.000 0.000 0.031 

lD.sexF 0.989 0.000 0.000 0.001 0.010 0.000 

lD.sexM 0.000 0.989 0.001 0.000 0.000 0.010 

lg.sexF 0.987 0.000 0.000 0.003 0.010 0.000 

lg.sexM 0.000 0.987 0.003 0.000 0.000 0.010 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.001 0.000 -0.006 0.000 -0.007 0.000 

lA.sexM 0.000 0.001 0.000 -0.006 0.000 -0.007 

lD.sexF -0.006 0.000 0.141 0.000 0.166 0.000 

lD.sexM 0.000 -0.006 0.000 0.141 0.000 0.166 

lg.sexF -0.007 0.000 0.166 0.000 0.204 0.000 

lg.sexM 0.000 -0.007 0.000 0.166 0.000 0.204 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.460 0.000 -0.415 0.000 

lA.sexM 0.000 1.000 0.000 -0.460 0.000 -0.415 

lD.sexF -0.460 0.000 1.000 0.000 0.976 0.000 

lD.sexM 0.000 -0.460 0.000 1.000 0.000 0.976 

lg.sexF -0.415 0.000 0.976 0.000 1.000 0.000 

lg.sexM 0.000 -0.415 0.000 0.976 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.595 0.037 

lA.sexM 1.595 0.037 

lD.sexF 0.778 0.376 

lD.sexM 0.778 0.376 

lg.sexF 0.907 0.451 
lg.sexM 0.907 0.451 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 2.1993035 

lA.sexM 2.1993035 

lD.sexF 0.4505348 

lD.sexM 0.4505348 

lg.sexF 0.3170473 

lg.sexM 0.3170473 

Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.930366 (0.1090588) 

BMD10 estimate (standard error): 2.174100 (0.5658673) 
Power parameter estimate (standard error): 2.473317 (0.790127) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.721489 4.930366 5.148483 

BMD 1.429808 2.174100 3.305834 

Power Parameter 1.509113 2.473317 4.053571 

 BMDL 

1.531176 
Chlorpyrifos 

MRID 48139301 

Acute Oral – Gavage 

Female PND 11 RBC ChE 

R-based single sex program

Fri Jan 21 09:23:46 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 5.752 5.752 1.500 1.500 1.000 1.000 

mu 0.248 0.248 0.951 0.951 1.426 1.426 

lA.sexF 0.000 0.185 0.000 0.778 0.037 0.000 

lA.sexM 0.185 0.000 0.778 0.000 0.000 0.037 

lD.sexF 0.000 0.971 0.000 0.002 0.027 0.000 

lD.sexM 0.971 0.000 0.002 0.000 0.000 0.027 

lg.sexF 0.000 0.960 0.000 0.013 0.027 0.000 

lg.sexM 0.960 0.000 0.013 0.000 0.000 0.027 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.001 0.000 -0.005 0.000 -0.003 0.000 

lA.sexM 0.000 0.001 0.000 -0.005 0.000 -0.003 

lD.sexF -0.005 0.000 0.097 0.000 0.069 0.000 

lD.sexM 0.000 -0.005 0.000 0.097 0.000 0.069 

lg.sexF -0.003 0.000 0.069 0.000 0.056 0.000 

lg.sexM 0.000 -0.003 0.000 0.069 0.000 0.056 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.434 0.000 -0.353 0.000 

lA.sexM 0.000 1.000 0.000 -0.434 0.000 -0.353 

lD.sexF -0.434 0.000 1.000 0.000 0.933 0.000 

lD.sexM 0.000 -0.434 0.000 1.000 0.000 0.933 

lg.sexF -0.353 0.000 0.933 0.000 1.000 0.000 

lg.sexM 0.000 -0.353 0.000 0.933 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.590 0.039 

lA.sexM 1.590 0.039 

lD.sexF -0.031 0.312 

lD.sexM -0.031 0.312 
lg.sexF 0.588 0.236 

lg.sexM 0.588 0.236 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.9530866 

lA.sexM 1.9530866 

lD.sexF 0.4152111 

lD.sexM 0.4152111 

lg.sexF 0.4827111 

lg.sexM 0.4827111 

 pnd female rbc che 
Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.905236 (0.1169381) 

BMD10 estimate (standard error): 0.9689255 (0.1676837) 

Power parameter estimate (standard error): 1.799136 (0.1841666) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.6820031 4.9052358 5.139112 

BMD 0.7197897 0.9689255 1.304293 

Power Parameter 1.4933959 1.7991357 2.167469 

 BMDL 

0.755621 
Chlorpyrifos 

MRID 48139301 

Acute Oral – Gavage 

Male PND 11 Brain ChE 

R-based single sex program

Fri Jan 21 08:37:26 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 9.307 9.307 1.521 1.521 1.000 1.000 

mu 0.155 0.155 0.948 0.948 1.441 1.441 

lA.sexF 0.207 0.000 0.760 0.000 0.000 0.033 

lA.sexM 0.000 0.207 0.000 0.760 0.033 0.000 

lD.sexF 0.988 0.000 0.001 0.000 0.000 0.010 

lD.sexM 0.000 0.988 0.000 0.001 0.010 0.000 

lg.sexF 0.986 0.000 0.004 0.000 0.000 0.010 

lg.sexM 0.000 0.986 0.000 0.004 0.010 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.001 0.000 -0.007 0.000 -0.007 0.000 

lA.sexM 0.000 0.001 0.000 -0.007 0.000 -0.007 

lD.sexF -0.007 0.000 0.159 0.000 0.181 0.000 

lD.sexM 0.000 -0.007 0.000 0.159 0.000 0.181 

lg.sexF -0.007 0.000 0.181 0.000 0.218 0.000 

lg.sexM 0.000 -0.007 0.000 0.181 0.000 0.218 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.465 0.000 -0.415 0.000 

lA.sexM 0.000 1.000 0.000 -0.465 0.000 -0.415 

lD.sexF -0.465 0.000 1.000 0.000 0.974 0.000 

lD.sexM 0.000 -0.465 0.000 1.000 0.000 0.974 

lg.sexF -0.415 0.000 0.974 0.000 1.000 0.000 

lg.sexM 0.000 -0.415 0.000 0.974 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.556 0.038 

lA.sexM 1.556 0.038 

lD.sexF 0.756 0.399 

lD.sexM 0.756 0.399 

lg.sexF 0.859 0.467 

lg.sexM 0.859 0.467 
---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 2.1195136 

lA.sexM 2.1195137 

lD.sexF 0.4147902 

lD.sexM 0.4147902 

lg.sexF 0.2949989 

lg.sexM 0.2949989 

Variance is homogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.741785 (0.09557595) 

BMD10 estimate (standard error): 2.129423 (0.5433625) 
Power parameter estimate (standard error): 2.359922 (0.7317253) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.558040 4.741785 4.932937 

BMD 1.409899 2.129423 3.216148 

Power Parameter 1.456628 2.359922 3.823372 

 BMDL 

1.508193 

 PND 11 Male Brain ChE Dose-Response Curve for Chlorpyrifos 
Chlorpyrifos 

MRID 48139301 

Acute Oral – Gavage 

Male PND 11 RBC ChE 

R-based single sex program

Fri Jan 21 09:02:30 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 6.080 6.080 1.513 1.513 1.000 1.000 

mu 0.235 0.235 0.946 0.946 1.431 1.431 

lA.sexF 0.201 0.000 0.761 0.000 0.000 0.038 

lA.sexM 0.000 0.201 0.000 0.761 0.038 0.000 

lD.sexF 0.974 0.000 0.002 0.000 0.000 0.024 

lD.sexM 0.000 0.974 0.000 0.002 0.024 0.000 

lg.sexF 0.964 0.000 0.012 0.000 0.000 0.024 

lg.sexM 0.000 0.964 0.000 0.012 0.024 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.002 0.000 -0.006 0.000 -0.004 0.000 

lA.sexM 0.000 0.002 0.000 -0.006 0.000 -0.004 

lD.sexF -0.006 0.000 0.121 0.000 0.080 0.000 

lD.sexM 0.000 -0.006 0.000 0.121 0.000 0.080 

lg.sexF -0.004 0.000 0.080 0.000 0.060 0.000 

lg.sexM 0.000 -0.004 0.000 0.080 0.000 0.060 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.454 0.000 -0.374 0.000 

lA.sexM 0.000 1.000 0.000 -0.454 0.000 -0.374 

lD.sexF -0.454 0.000 1.000 0.000 0.940 0.000 

lD.sexM 0.000 -0.454 0.000 1.000 0.000 0.940 

lg.sexF -0.374 0.000 0.940 0.000 1.000 0.000 

lg.sexM 0.000 -0.374 0.000 0.940 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.584 0.040 

lA.sexM 1.584 0.040 

lD.sexF -0.211 0.348 

lD.sexM -0.211 0.348 
lg.sexF 0.502 0.245 

lg.sexM 0.502 0.245 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.9059388 

lA.sexM 1.9059388 

lD.sexF 0.3995886 

lD.sexM 0.3995886 

lg.sexF 0.5079132 

lg.sexM 0.5079132 

Variance is heterogeneous ::: Power parameter fit 
Intercept estimate (standard error): 4.868777 (0.1039630) 

BMD10 estimate (standard error): 0.825921 (0.1277144) 

Power parameter estimate (standard error): 1.678496 (0.1418870) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.6693674 4.868777 5.076703 

BMD 0.6308277 0.825921 1.081350 

Power Parameter 1.4357601 1.678496 1.962270 

 BMDL 

0.6592323 
91107 acute oral (gavage) RBC F (PND) 8

Exponential model 

Model variance 

Bmr 10 

Risk – relative 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 07 14:30:28 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 4.09606 4.09606 4.09606 

4.09606 
 rho 1.03903 1.03903 1.03903 

1.03903 

 a 2627.12 2627.12 7007.18 

7007.18 

 b 0.397597 0.397597 0.38917 

0.38917 

 c -- -- 0.000124629 

0.000124629 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 11.3065 3.28542 11.3065 

3.22766 

 rho 0.255862 1.14546 0.255862 

1.152 

 a 6853.22 6553.13 6853.22 

6525.17 

 b 0.300822 0.296078 0.300822 

0.359141 

 c -- -- 0 

0.117855 

 d -- 1.78248 -- 

2.35354 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 6432 776 

 0.05 8 6674 633.6 

 0.1 8 6518 677.1 

 0.5 8 6345 1308 

 2 8 4441 392.2 

 5 8 873.3 281.5 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 6853 882.9 -1.351 

 0.05 6751 881.2 -0.2485 

 0.1 6650 879.5 -0.4249 

 0.5 5896 866.1 1.466 

 2 3755 817.5 2.373 

 5 1523 728.3 -2.523 

 3 0 6553 792.9 -0.4339 

 0.05 6550 792.6 0.4423 

 0.1 6541 792 -0.08138 

 0.5 6339 777.9 0.02145 

 2 4424 633.1 0.07671 

 5 876 250.4 -0.03085 

 4 0 6853 882.9 -1.351 

 0.05 6751 881.2 -0.2485 
 0.1 6650 879.5 -0.4249 

 0.5 5896 866.1 1.466 

 2 3755 817.5 2.373 

 5 1523 728.3 -2.523 

 5 0 6525 790.8 -0.335 

 0.05 6525 790.8 0.5321 

 0.1 6523 790.7 -0.01746 

 0.5 6425 783.8 -0.2884 

 2 4407 630.8 0.1537 

 5 878.1 249.1 -0.05447 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -338.7709 7 691.5419 

 A2 -328.0852 12 680.1704 

 A3 -333.0133 8 682.0267 

 R -393.3941 2 790.7883 

 2 -347.2382 4 702.4764 

 3 -333.2483 5 676.4965 

 4 -347.2382 4 702.4764 

 5 -333.1838 6 678.3676 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 
 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 130.6 10 < 0.0001 

 Test 2 21.37 5 0.000689 

 Test 3 9.856 4 0.04292 

 Test 4 28.45 4 < 0.0001 

 Test 5a 0.4698 3 0.9255 

 Test 5b 27.98 1 < 0.0001 

 Test 6a 28.45 4 < 0.0001 

 Test 6b -2.274e-013 0 N/A 

 Test 7a 0.3409 2 0.8433 

 Test 7b 0.1289 1 0.7196 

 Test 7c 28.11 2 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 
 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.350243 0.293888 

 3 0.955651 0.745873 

 4 0.350243 0.293888 

 5 1.13233 0.750633 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

14:30 09/07 2010

BMDL BMD

 

Exponential
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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

14:30 09/07 2010

BMDL BMD

 

Exponential

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

14:30 09/07 2010

BMDL BMD

 

Exponential
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dose

Exponential Model 5 with 0.95 Confidence Level

14:30 09/07 2010

BMDL BMD

 

Exponential
91107 acute oral (gavage) RBC 

M 

(PND) 8

Exponential model 

Model variance 

Bmr 10 

Risk – relative 

!!! Warning: optimum may not have been found for Model 3 !!! 

!!! Bad completion code in maximum likelihood optimization routine !!! 

!!! Try choosing different initial values !!! 

Warning: optimum may not have been found form model 2 (BMDL). Bad process 

completion in Optimization routine. 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 07 14:43:34 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 
 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 3.94441 3.94441 3.94441 

3.94441 

 rho 1.05495 1.05495 1.05495 

1.05495 

 a 2597.2 2597.2 7554.54 

7554.54 

 b 0.428325 0.428325 0.422074 

0.422074 

 c -- -- 0.000106466 

0.000106466 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 11.6629 3.94441 11.6629 

3.5187 

 rho 0.19443 1.05495 0.19443 

1.09804 

 a 7312.89 2597.2 7312.89 

7009.08 

 b 0.324311 0.428325 0.324311 

0.317003 

 c -- -- 0 

3.77349e-025 

 d -- 1 -- 

1.67104 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 6891 782.3 

 0.05 8 7034 923.3 

 0.1 8 7195 860.7 

 0.5 8 6538 544.3 

 2 8 4434 590.1 

 5 8 804.3 248.8 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 7313 809.5 -1.474 

 0.05 7195 808.2 -0.5644 
 0.1 7080 806.9 0.404 

 0.5 6218 796.8 1.133 

 2 3823 760 2.272 

 5 1445 691.4 -2.621 

 3 0 0 0 1.#IO 

 0.05 0 0 1.#IO 

 0.1 0 0 1.#IO 

 0.5 0 0 1.#IO 

 2 0 0 1.#IO 

 5 0 0 1.#IO 

 4 0 7313 809.5 -1.474 

 0.05 7195 808.2 -0.5644 

 0.1 7080 806.9 0.404 

 0.5 6218 796.8 1.133 

 2 3823 760 2.272 

 5 1445 691.4 -2.621 

 5 0 7009 750.6 -0.4449 

 0.05 7002 750.2 0.1199 

 0.1 6987 749.3 0.7836 

 0.5 6694 731.9 -0.6034 

 2 4394 580.9 0.1924 

 5 809.1 229.4 -0.05904 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -335.0147 7 684.0293 

 A2 -328.3503 12 680.7006 

 A3 -329.3416 8 674.6833 

 R -397.1813 2 798.3625 

 2 -343.4978 4 694.9957 

 3 -2609.113 4 5226.226 

 4 -343.4978 4 694.9957 

 5 -330.0495 6 672.099 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 
 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 137.7 10 < 0.0001 

 Test 2 13.33 5 0.02049 

 Test 3 1.983 4 0.7389 

 Test 4 28.31 4 < 0.0001 

 Test 5a 4560 4 < 0.0001 

 Test 5b -4531 0 N/A 

 Test 6a 28.31 4 < 0.0001 

 Test 6b 0 0 N/A 

 Test 7a 1.416 2 0.4927 

 Test 7b 4558 2 < 0.0001 

 Test 7c 26.9 2 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 
 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.324875 0.279152 

 3 0.245983 Bad completion 

 4 0.324875 0.284467 

 5 0.820499 0.652608 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

14:43 09/07 2010

BMDL BMD

 

Exponential
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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

14:43 09/07 2010

BMDL BMD

 

Exponential

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Mean Response

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Exponential Model 4 with 0.95 Confidence Level

14:43 09/07 2010

BMDL BMD

 

Exponential
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Exponential Model 5 with 0.95 Confidence Level

14:43 09/07 2010

BMDL BMD

 

Exponential
Chlorpyrifos 91107 acute oral (milk) Brain F (PND) 8 

Chlorpyrifos 91107 acute oral (milk) Brain F (PND) 8 

time 

(hrs) 

BMDS Exponential MODEL variance 

Bmr = 10% 

brain 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Aug 19 12:45:48 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 37.0859 37.0859 37.0859 

37.0859 
 rho -2.10567 -2.10567 -2.10567 -

2.10567 

 a 18140.5 18140.5 28317.9 

28317.9 

 b 0.165586 0.165586 0.171736 

0.171736 

 c -- -- 0.000406503 

0.000406503 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 46.7917 34.5211 46.7917 

37.4376 

 rho -3.04757 -1.84029 -3.04757 -

2.13239 

 a 26995.9 26650.9 26995.9 

26574.5 

 b 0.121787 0.177263 0.121787 

0.468568 

 c -- -- 0 

0.462647 

 d -- 1.63396 -- 

13.4446 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 2.641e+004 3026 

 0.05 8 2.611e+004 2979 

 0.1 8 2.661e+004 2105 

 0.5 8 2.697e+004 1413 

 2 8 2.22e+004 4811 

 5 8 1.151e+004 5332 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2.7e+004 2561 -0.6471 

 0.05 2.683e+004 2584 -0.7866 

 0.1 2.667e+004 2608 -0.0653 

 0.5 2.54e+004 2809 1.579 

 2 2.116e+004 3711 0.7934 

 5 1.468e+004 6476 -1.386 

 3 0 2.665e+004 2654 -0.2567 

 0.05 2.664e+004 2655 -0.5602 

 0.1 2.661e+004 2657 -0.005741 

 0.5 2.615e+004 2701 0.8608 

 2 2.218e+004 3143 0.02053 

 5 1.173e+004 5649 -0.1074 

 4 0 2.7e+004 2561 -0.6471 

 0.05 2.683e+004 2584 -0.7866 
 0.1 2.667e+004 2608 -0.0653 

 0.5 2.54e+004 2809 1.579 

 2 2.116e+004 3711 0.7934 

 5 1.468e+004 6476 -1.386 

 5 0 2.657e+004 2583 -0.18 

 0.05 2.657e+004 2583 -0.505 

 0.1 2.657e+004 2583 0.03767 

 0.5 2.657e+004 2583 0.4324 

 2 2.17e+004 3206 0.4433 

 5 1.229e+004 5876 -0.3771 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -413.3148 7 840.6295 

 A2 -404.6353 12 833.2706 

 A3 -409.0128 8 834.0255 

 R -444.9614 2 893.9228 

 2 -412.0599 4 832.1199 

 3 -409.9703 5 829.9406 

 4 -412.0599 4 832.1199 

 5 -409.4277 6 830.8553 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 
 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 80.65 10 < 0.0001 

 Test 2 17.36 5 0.003867 

 Test 3 8.755 4 0.06753 

 Test 4 6.094 4 0.1922 

 Test 5a 1.915 3 0.5902 

 Test 5b 4.179 1 0.04092 

 Test 6a 6.094 4 0.1922 

 Test 6b -1.137e-013 0 N/A 

 Test 7a 0.8298 2 0.6604 

 Test 7b 1.085 1 0.2975 

 Test 7c 5.265 2 0.07192 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 
 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.865123 0.648309 

 3 1.42315 0.911925 

 4 0.865123 0.639232 

 5 1.89749 1.02616 

 5000

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

12:45 08/19 2010

BMDL BMD

 

Exponential
 5000

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:45 08/19 2010

BMDL BMD

 

Exponential

 5000

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:45 08/19 2010

BMDL BMD

 

Exponential
 5000

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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

12:45 08/19 2010

BMDL BMD

 

Exponential
Chlorpyrifos 48139301 acute oral (milk) Brain 

M (PND) 8 0 23910.8 U/L 1872.8 8 

BmdsExponentialModel var ====================================================================  Exponential Model. (Version: 1.7; Date: 12/10/2009)  Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d)  Gnuplot Plotting File:  Mon Jan 31 09:39:23 2011  ====================================================================  BMDS Model Run ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  The form of the response function by Model:  Model 2: Y[dose] = a * exp{sign * b * dose}  Model 3: Y[dose] = a * exp{sign * (b * dose)^d}  Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}]  Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]  Note: Y[dose] is the median response for exposure = dose;  sign = +1 for increasing trend in data;  sign = -1 for decreasing trend.  Model 2 is nested within Models 3 and 4.  Model 3 is nested within Model 5.  Model 4 is nested within Model 5. 
 Dependent variable = Mean 

 Independent variable = Dose  Data are assumed to be distributed: normally  Variance Model: exp(lnalpha +rho *ln(Y[dose]))  The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)  Total number of dose groups = 6  Total number of records with missing values = 0  Maximum number of iterations = 250  Relative Function Convergence has been set to: 1e-008  Parameter Convergence has been set to: 1e-008  MLE solution provided: Exact  Initial Parameter Values  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 37.8267 37.8267 37.8267 37.8267  rho -2.18811 -2.18811 -2.18811 -2.18811  a 19562.5 19562.5 26590.8 26590.8  b 0.109158 0.109158 0.116719 0.116719  c -- -- 0.000523429 0.000523429  d -- 1 -- 1  Parameter Estimates by Model  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 77.061 36.1419 77.0609 36.1419  rho -6.06079 -2.02234 -6.06079 -2.02234  a 25091.2 24752.7 25091.2 24752.7  b 0.0616144 0.191937 0.0616144 0.192434  c -- -- 0 0.00042597  d -- 13.4171 -- 14.3014 
 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev  ----- --- ---------- -------------  0 8 2.391e+004 1873  0.05 8 2.449e+004 3432  0.1 8 2.528e+004 2155  0.5 8 2.532e+004 2815  2 8 2.476e+004 2744  5 8 1.392e+004 4867  Estimated Values of Interest  Model Dose Est Mean Est Std Scaled Residual  ------- ------ ---------- --------- ----------------  2 0 2.509e+004 2519 -1.325  0.05 2.501e+004 2543 -0.5838  0.1 2.494e+004 2567 0.3766  0.5 2.433e+004 2766 1.017  2 2.218e+004 3660 1.992  5 1.844e+004 6408 -1.995  3 0 2.475e+004 2543 -0.9362  0.05 2.475e+004 2543 -0.2931  0.1 2.475e+004 2543 0.5851  0.5 2.475e+004 2543 0.636  2 2.475e+004 2543 0.00831  5 1.392e+004 4552 -1.248e-007  4 0 2.509e+004 2519 -1.325  0.05 2.501e+004 2543 -0.5838  0.1 2.494e+004 2567 0.3766  0.5 2.433e+004 2766 1.017  2 2.218e+004 3660 1.992  5 1.844e+004 6408 -1.995  5 0 2.475e+004 2543 -0.9362  0.05 2.475e+004 2543 -0.2931  0.1 2.475e+004 2543 0.5851  0.5 2.475e+004 2543 0.636  2 2.475e+004 2543 0.008277  5 1.392e+004 4552 -6.062e-008 
 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij)  Var{e(ij)} = Sigma^2  Model A2: Yij = Mu(i) + e(ij)  Var{e(ij)} = Sigma(i)^2  Model A3: Yij = Mu(i) + e(ij)  Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)  Model R: Yij = Mu + e(i)  Var{e(ij)} = Sigma^2  Likelihoods of Interest  Model Log(likelihood) DF AIC  ------- ----------------- ---- ------------  A1 -407.2546 7 828.5092  A2 -402.4099 12 828.8198  A3 -404.19 8 824.3801  R -432.9667 2 869.9334  2 -411.3514 4 830.7029  3 -405.0374 5 820.0748  4 -411.3514 4 830.7029  5 -405.0374 6 822.0748  Additive constant for all log-likelihoods = -44.11. This constant added to the  above values gives the log-likelihood including the term that does not  depend on the model parameters.  Explanation of Tests  Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R)  Test 2: Are Variances Homogeneous? (A2 vs. A1)  Test 3: Are variances adequately modeled? (A2 vs. A3)  Test 4: Does Model 2 fit the data? (A3 vs. 2) 
 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2)  Test 6a: Does Model 4 fit the data? (A3 vs 4)  Test 6b: Is Model 4 better than Model 2? (4 vs. 2)  Test 7a: Does Model 5 fit the data? (A3 vs 5)  Test 7b: Is Model 5 better than Model 3? (5 vs. 3)  Test 7c: Is Model 5 better than Model 4? (5 vs. 4)  Tests of Interest  Test -2*log(Likelihood Ratio) D. F. p-value  -------- ------------------------ ------ --------------  Test 1 61.11 10 < 0.0001  Test 2 9.689 5 0.08453  Test 3 3.56 4 0.4688  Test 4 14.32 4 0.006333  Test 5a 1.695 3 0.6381  Test 5b 12.63 1 0.00038  Test 6a 14.32 4 0.006333  Test 6b 3.411e-013 0 N/A  Test 7a 1.695 2 0.4285  Test 7b 2.377e-007 1 0.9996  Test 7c 12.63 2 0.001811  The p-value for Test 1 is less than .05. There appears to be a  difference between response and/or variances among the dose  levels, it seems appropriate to model the data.  The p-value for Test 2 is less than .1. A non-homogeneous  variance model appears to be appropriate.  The p-value for Test 3 is greater than .1. The modeled  variance appears to be appropriate here.  The p-value for Test 4 is less than .1. Model 2 may not adequately  describe the data; you may want to consider another model.  The p-value for Test 5a is greater than .1. Model 3 seems 
 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears  to fit the data better than Model 2.  The p-value for Test 6a is less than .1. Model 4 may not adequately  describe the data; you may want to consider another model.  Degrees of freedom for Test 6b are less than or equal to 0.  The Chi-Square test for fit is not valid.  The p-value for Test 7a is greater than .1. Model 5 seems  to adequately describe the data.  The p-value for Test 7b is greater than .05. Model 5 does  not seem to fit the data better than Model 3.  The p-value for Test 7c is less than .05. Model 5 appears  to fit the data better than Model 4.  Benchmark Dose Computations:  Specified Effect = 0.100000  Risk Type = Relative deviation  Confidence Level = 0.950000  BMD and BMDL by Model  Model BMD BMDL  ------- ------------ ----------  2 1.71 1.06761  3 4.40555 2.37247  4 1.71 1.06761  5 4.44013 2.37245 
 10000

 15000 20000 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:39 01/31 2011

BMDL BMD

Exponential
 10000

 15000 20000 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:39 01/31 2011

BMDL BMD

Exponential
 10000

 15000 20000 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:39 01/31 2011

BMDL BMD

Exponential
 10000

 15000 20000 25000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:39 01/31 2011

BMDL BMD

Exponential

Chlorpyrifos 48139301 acute oral (milk) RBC 

M (PND) 8 0 6304.5 U/L 1459.8 8 

Exponential Constant var  ====================================================================  Exponential Model. (Version: 1.7; Date: 12/10/2009)  Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d)  Gnuplot Plotting File:  Mon Jan 31 09:55:32 2011  ====================================================================  BMDS Model Run ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  The form of the response function by Model:  Model 2: Y[dose] = a * exp{sign * b * dose}  Model 3: Y[dose] = a * exp{sign * (b * dose)^d}  Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}]  Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]  Note: Y[dose] is the median response for exposure = dose;  sign = +1 for increasing trend in data;  sign = -1 for decreasing trend.  Model 2 is nested within Models 3 and 4.  Model 3 is nested within Model 5.  Model 4 is nested within Model 5.  Dependent variable = Mean  Independent variable = Dose  Data are assumed to be distributed: normally  Variance Model: exp(lnalpha +rho *ln(Y[dose])) 
 rho is set to 0. 

 A constant variance model is fit.  Total number of dose groups = 6  Total number of records with missing values = 0  Maximum number of iterations = 250  Relative Function Convergence has been set to: 1e-008  Parameter Convergence has been set to: 1e-008  MLE solution provided: Exact  Initial Parameter Values  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 14.4191 14.4191 14.4191 14.4191  rho(S) 0 0 0 0  a 3505.76 3505.76 6920.34 6920.34  b 0.251523 0.251523 0.26177 0.26177  c -- -- 0.000260825 0.000260825  d -- 1 -- 1  (S) = Specified  Parameter Estimates by Model  Variable Model 2 Model 3 Model 4 Model 5  -------- ------- ------- ------- -------  lnalpha 14.4816 14.4412 14.4816 14.434  rho 0 0 0 0  a 6538.07 6365.61 6538.07 6308.03  b 0.222939 0.237027 0.222939 0.4727  c -- -- 0 0.286143  d -- 1.47378 -- 11.3456  Table of Stats From Input Data  Dose N Obs Mean Obs Std Dev 
 ----- --- ---------- ------------- 

 0 8 6305 1460  0.05 8 6017 1385  0.1 8 6591 923.9  0.5 8 6320 1028  2 8 4459 1916  5 8 1805 1706  Estimated Values of Interest  Model Dose Est Mean Est Std Scaled Residual  ------- ------ ---------- --------- ----------------  2 0 6538 1395 -0.4735  0.05 6466 1395 -0.9104  0.1 6394 1395 0.3991  0.5 5848 1395 0.9566  2 4186 1395 0.5523  5 2145 1395 -0.6884  3 0 6366 1367 -0.1264  0.05 6356 1367 -0.7031  0.1 6340 1367 0.5187  0.5 6097 1367 0.4623  2 4563 1367 -0.217  5 1762 1367 0.0889  4 0 6538 1395 -0.4735  0.05 6466 1395 -0.9104  0.1 6394 1395 0.3991  0.5 5848 1395 0.9566  2 4186 1395 0.5523  5 2145 1395 -0.6884  5 0 6308 1362 -0.007318  0.05 6308 1362 -0.6052  0.1 6308 1362 0.5871  0.5 6308 1362 0.02548  2 4458 1362 2.577e-009  5 1805 1362 3.2e-008  Other models for which likelihoods are calculated: 
 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2  Model A2: Yij = Mu(i) + e(ij)  Var{e(ij)} = Sigma(i)^2  Model A3: Yij = Mu(i) + e(ij)  Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)  Model R: Yij = Mu + e(i)  Var{e(ij)} = Sigma^2  Likelihoods of Interest  Model Log(likelihood) DF AIC  ------- ----------------- ---- ------------  A1 -370.0582 7 754.1163  A2 -367.0576 12 758.1151  A3 -370.0582 7 754.1163  R -392.6363 2 789.2727  2 -371.5573 3 749.1146  3 -370.5881 4 749.1761  4 -371.5573 3 749.1146  5 -370.4166 5 750.8333  Additive constant for all log-likelihoods = -44.11. This constant added to the  above values gives the log-likelihood including the term that does not  depend on the model parameters.  Explanation of Tests  Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R)  Test 2: Are Variances Homogeneous? (A2 vs. A1)  Test 3: Are variances adequately modeled? (A2 vs. A3)  Test 4: Does Model 2 fit the data? (A3 vs. 2)  Test 5a: Does Model 3 fit the data? (A3 vs 3)  Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 
 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2)  Test 7a: Does Model 5 fit the data? (A3 vs 5)  Test 7b: Is Model 5 better than Model 3? (5 vs. 3)  Test 7c: Is Model 5 better than Model 4? (5 vs. 4)  Tests of Interest  Test -2*log(Likelihood Ratio) D. F. p-value  -------- ------------------------ ------ --------------  Test 1 51.16 10 < 0.0001  Test 2 6.001 5 0.3061  Test 3 6.001 5 0.3061  Test 4 2.998 4 0.5581  Test 5a 1.06 3 0.7868  Test 5b 1.938 1 0.1638  Test 6a 2.998 4 0.5581  Test 6b 0 0 N/A  Test 7a 0.7169 2 0.6987  Test 7b 0.3428 1 0.5582  Test 7c 2.281 2 0.3196  The p-value for Test 1 is less than .05. There appears to be a  difference between response and/or variances among the dose  levels, it seems appropriate to model the data.  The p-value for Test 2 is greater than .1. A homogeneous  variance model appears to be appropriate here.  The p-value for Test 3 is greater than .1. The modeled  variance appears to be appropriate here.  The p-value for Test 4 is greater than .1. Model 2 seems  to adequately describe the data.  The p-value for Test 5a is greater than .1. Model 3 seems  to adequately describe the data.  The p-value for Test 5b is greater than .05. Model 3 does 
 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems  to adequately describe the data.  Degrees of freedom for Test 6b are less than or equal to 0.  The Chi-Square test for fit is not valid.  The p-value for Test 7a is greater than .1. Model 5 seems  to adequately describe the data.  The p-value for Test 7b is greater than .05. Model 5 does  not seem to fit the data better than Model 3.  The p-value for Test 7c is greater than .05. Model 5 does  not seem to fit the data better than Model 4.  Benchmark Dose Computations:  Specified Effect = 0.100000  Risk Type = Relative deviation  Confidence Level = 0.950000  BMD and BMDL by Model  Model BMD BMDL  ------- ------------ ----------  2 0.472598 0.359944  3 0.916354 0.408519  4 0.472598 0.337649  5 1.79072 0.423913 
 0

 1000 2000 3000 4000 5000 6000 7000 8000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:55 01/31 2011

BMDL BMD

Exponential
 0

 1000 2000 3000 4000 5000 6000 7000 8000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:55 01/31 2011

BMDL BMD

Exponential
 0

 1000 2000 3000 4000 5000 6000 7000 8000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:55 01/31 2011

BMDL BMD

Exponential
 0

 1000 2000 3000 4000 5000 6000 7000 8000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:55 01/31 2011

BMDL BMD

Exponential
Chlorpyrifos 48139301 acute oral (milk) RBC 

M 

(PND) 8

0 6304.5 U/L 1459.8 8 RAT 

R based model 

Acute milk 

Male 

Rbc 

Mon Jan 31 10:06:04 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 7.297 7.297 1.542 1.542 1.000 1.000 

mu 0.198 0.198 0.936 0.936 1.444 1.444 

lA.sexF 0.252 0.000 0.000 0.710 0.000 0.038 

lA.sexM 0.000 0.252 0.710 0.000 0.038 0.000 

lD.sexF 0.982 0.000 0.000 0.002 0.000 0.016 

lD.sexM 0.000 0.982 0.002 0.000 0.016 0.000 

lg.sexF 0.974 0.000 0.000 0.009 0.000 0.017 

lg.sexM 0.000 0.974 0.009 0.000 0.017 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.002 0.000 -0.009 0.000 -0.005 0.000 

lA.sexM 0.000 0.002 0.000 -0.009 0.000 -0.005 

lD.sexF -0.009 0.000 0.179 0.000 0.112 0.000 

lD.sexM 0.000 -0.009 0.000 0.179 0.000 0.112 

lg.sexF -0.005 0.000 0.112 0.000 0.076 0.000 

lg.sexM 0.000 -0.005 0.000 0.112 0.000 0.076 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.509 0.000 -0.438 0.000 

lA.sexM 0.000 1.000 0.000 -0.509 0.000 -0.438 

lD.sexF -0.509 0.000 1.000 0.000 0.957 0.000 

lD.sexM 0.000 -0.509 0.000 1.000 0.000 0.957 

lg.sexF -0.438 0.000 0.957 0.000 1.000 0.000 

lg.sexM 0.000 -0.438 0.000 0.957 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.575 0.041 

lA.sexM 1.575 0.041 
lD.sexF -0.088 0.423 

lD.sexM -0.088 0.423 

lg.sexF 0.388 0.276 

lg.sexM 0.388 0.276 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.9904167 

lA.sexM 1.9904167 

lD.sexF 0.3800440 

lD.sexM 0.3800440 

lg.sexF 0.4907534 

lg.sexM 0.4907534 
0123456

0 2000 4000 6000

Male dose-respose curve for chlorpyrifos

dose

Response

BMD

BMDL

Variance is heterogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 4.935243 (0.1921248) 

BMD10 estimate (standard error): 0.5006536 (0.1322799) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.5787490 4.9352427 5.3194924 

BMD 0.3275609 0.5006536 0.7652135 

 BMDL 

0.3510713 

> 
Chlorpyrifos 

MRID 

91107 acute oral (milk) RBC F (PND) 8 

BMDS Exponential MODEL variance 

Bmr = 10% 

Warning: optimum may not have been found form model 2 (BMDL). Bad process 

completion in Optimization routine. 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Aug 19 12:17:53 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 
 lnalpha 21.2166 21.2166 21.2166 

21.2166 

 rho -0.856979 -0.856979 -0.856979 -

0.856979 

 a 3134.79 108.759 6825.84 

6825.84 

 b 0.300681 -0.156798 0.303809 

0.303809 

 c -- -- 0.000201411 

0.000201411 

 d -- 2 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 27.6204 -21.9981 27.6204 

22.6491 

 rho -1.58793 4.37075 -1.58793 

-1.0078 

 a 6447.31 5145.45 6447.31 

6362.67 

 b 0.210731 0.00531847 0.210731 

0.239615 

 c -- -- 0 

1.06378e-026 

 d -- 8.74725 -- 

1.3054 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 6324 648.1 

 0.05 8 6197 613.7 

 0.1 8 6501 978.9 

 0.5 8 5877 1505 

 2 8 4600 1759 

 5 8 1375 1634 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 6447 940.1 -0.371 

 0.05 6380 948 -0.5467 

 0.1 6313 956 0.556 

 0.5 5803 1022 0.2055 

 2 4230 1314 0.7962 

 5 2248 2170 -1.138 

 3 0 5145 2158 1.545 

 0.05 5145 2158 1.378 

 0.1 5145 2158 1.776 

 0.5 5145 2158 0.9586 

 2 5145 2158 -0.7152 

 5 5145 2158 -4.942 
 4 0 6447 940.1 -0.371 

 0.05 6380 948 -0.5467 

 0.1 6313 956 0.556 

 0.5 5803 1022 0.2055 

 2 4230 1314 0.7962 

 5 2248 2170 -1.138 

 5 0 6363 1004 -0.109 

 0.05 6343 1005 -0.4122 

 0.1 6314 1007 0.5243 

 0.5 5976 1036 -0.2713 

 2 4339 1217 0.6062 

 5 1794 1899 -0.6241 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -364.0961 7 742.1921 

 A2 -356.5333 12 737.0666 

 A3 -362.3269 8 740.6539 

 R -392.491 2 788.9821 

 2 -362.8476 4 733.6952 

 3 -392.491 5 794.9821 

 4 -362.8476 4 733.6952 

 5 -362.6837 6 737.3674 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 
 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 71.92 10 < 0.0001 

 Test 2 15.13 5 0.009839 

 Test 3 11.59 4 0.0207 

 Test 4 1.041 4 0.9035 

 Test 5a 60.33 3 < 0.0001 

 Test 5b -59.29 1 N/A 

 Test 6a 1.041 4 0.9035 

 Test 6b 0 0 N/A 

 Test 7a 0.7136 2 0.6999 

 Test 7b 59.61 1 < 0.0001 

 Test 7c 0.3278 2 0.8488 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 
 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.499976 0.364778 

 3 145.373 Bad completion 

 4 0.499976 0.283632 

 5 0.744403 0.297474 

 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

12:18 08/19 2010

BMDL BMD

 

Exponential
 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 20 40 60 80 100 120 140

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:18 08/19 2010

BMDL BMD

 

Exponential

 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:18 08/19 2010

BMDL BMD

 

Exponential
 0

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 2000

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 4000

 5000

 6000

 7000

 8000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

12:18 08/19 2010

BMDL BMD

 

Exponential
Chlorpyrifos 91107 

Repeat oral (Corn Oil) 

Brain F (PND) 8 

time 

(hrs) 

BMDS Exponential CONSTANT variance 

Bmr = 10% 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Aug 20 09:45:34 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 15.0166 15.0166 15.0166 

15.0166 
 rho(S) 0 0 0 

0 

 a 28366.3 28366.3 46962.1 

46962.1 

 b 0.27005 0.27005 0.294563 

0.294563 

 c -- -- 0.0184661 

0.0184661 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 15.6891 15.5072 15.6891 

15.1553 

 rho 0 0 0 

0 

 a 44855.1 43998.3 44855.1 

43504.8 

 b 0.258011 0.273817 0.258011 

0.813136 

 c -- -- 0 

0.398671 

 d -- 1.24794 -- 

4.0167 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 4.229e+004 1616 

 0.05 8 4.473e+004 2598 

 0.1 8 4.35e+004 1409 

 0.5 8 4.281e+004 1258 

 1 8 3.427e+004 2009 

 3.5 8 1.734e+004 2414 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.486e+004 2552 -2.844 

 0.05 4.428e+004 2552 0.494 

 0.1 4.371e+004 2552 -0.2372 

 0.5 3.943e+004 2552 3.75 

 1 3.465e+004 2552 -0.4308 

 3.5 1.818e+004 2552 -0.9281 

 3 0 4.4e+004 2330 -2.075 

 0.05 4.379e+004 2330 1.135 

 0.1 4.351e+004 2330 -0.01066 

 0.5 4.047e+004 2330 2.842 

 1 3.607e+004 2330 -2.194 

 3.5 1.704e+004 2330 0.3633 
 4 0 4.486e+004 2552 -2.844 

 0.05 4.428e+004 2552 0.494 

 0.1 4.371e+004 2552 -0.2372 

 0.5 3.943e+004 2552 3.75 

 1 3.465e+004 2552 -0.4308 

 3.5 1.818e+004 2552 -0.9281 

 5 0 4.35e+004 1954 -1.759 

 0.05 4.35e+004 1954 1.767 

 0.1 4.35e+004 1954 -0.007437 

 0.5 4.281e+004 1954 -0.0007156 

 1 3.427e+004 1954 5.139e-005 

 3.5 1.734e+004 1954 -3.384e-006 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -384.3978 7 782.7956 

 A2 -381.053 12 786.106 

 A3 -384.3978 7 782.7956 

 R -465.1161 2 934.2321 

 2 -400.5375 3 807.075 

 3 -396.1729 4 800.3459 

 4 -400.5375 3 807.075 

 5 -387.7279 5 785.4558 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 
 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 168.1 10 < 0.0001 

 Test 2 6.69 5 0.2448 

 Test 3 6.69 5 0.2448 

 Test 4 32.28 4 < 0.0001 

 Test 5a 23.55 3 < 0.0001 

 Test 5b 8.729 1 0.003132 

 Test 6a 32.28 4 < 0.0001 

 Test 6b -1.137e-013 0 N/A 

 Test 7a 6.66 2 0.03579 

 Test 7b 16.89 1 < 0.0001 

 Test 7c 25.62 2 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 
 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.408357 0.374977 

 3 0.601713 0.48523 

 4 0.408357 0.372758 

 5 0.804549 0.692453 

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 50000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:45 08/20 2010

BMDL BMD

 

Exponential
 15000

 20000

 25000

 30000

 35000

 40000

 45000

 50000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:45 08/20 2010

BMDL BMD

 

Exponential

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 50000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:45 08/20 2010

BMDL BMD

 

Exponential
 15000

 20000

 25000

 30000

 35000

 40000

 45000

 50000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:45 08/20 2010

BMDL BMD

 

Exponential
Chlorpyrifos 91107 

Repeat oral (Corn Oil) 

Brain 

M 

(PND) 8 

time 

(hrs) 

BMDS Exponential MODEL variance 

Bmr = 10% 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Aug 23 10:55:35 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 6.42703 6.42703 6.42703 

6.42703 
 rho 0.823626 0.823626 0.823626 

0.823626 

 a 25186.2 25186.2 45736.3 

45736.3 

 b 0.332222 0.332222 0.352184 

0.352184 

 c -- -- 0.0151936 

0.0151936 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 2.52917 3.6154 2.52917 

10.209 

 rho 1.25748 1.148 1.25748 

0.489746 

 a 44537.8 43863.5 44537.8 

43166.6 

 b 0.330788 0.326994 0.330788 

0.785613 

 c -- -- 0 

0.321121 

 d -- 1.11249 -- 

2.62367 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 4.326e+004 1500 

 0.05 8 4.356e+004 1981 

 0.1 8 4.266e+004 1576 

 0.5 8 4.078e+004 1865 

 1 8 3.101e+004 4154 

 3.5 8 1.39e+004 987.9 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.454e+004 2965 -1.215 

 0.05 4.381e+004 2934 -0.2399 

 0.1 4.309e+004 2904 -0.4206 

 0.5 3.775e+004 2672 3.209 

 1 3.199e+004 2408 -1.161 

 3.5 1.399e+004 1432 -0.1883 

 3 0 4.386e+004 2816 -0.6017 

 0.05 4.341e+004 2800 0.1455 

 0.1 4.29e+004 2780 -0.2455 

 0.5 3.839e+004 2609 2.594 

 1 3.288e+004 2387 -2.216 

 3.5 1.372e+004 1445 0.3418 

 4 0 4.454e+004 2965 -1.215 

 0.05 4.381e+004 2934 -0.2399 
 0.1 4.309e+004 2904 -0.4206 

 0.5 3.775e+004 2672 3.209 

 1 3.199e+004 2408 -1.161 

 3.5 1.399e+004 1432 -0.1883 

 5 0 4.317e+004 2248 0.1232 

 0.05 4.316e+004 2248 0.5004 

 0.1 4.313e+004 2248 -0.5949 

 0.5 4.075e+004 2217 0.04103 

 1 3.109e+004 2075 -0.1203 

 3.5 1.386e+004 1702 0.06025 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -391.2882 7 796.5765 

 A2 -381.0007 12 786.0015 

 A3 -391.1629 8 798.3258 

 R -470.3705 2 944.7409 

 2 -399.175 4 806.3501 

 3 -397.8473 5 805.6945 

 4 -399.175 4 806.3501 

 5 -391.4813 6 794.9625 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 
 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 178.7 10 < 0.0001 

 Test 2 20.58 5 0.0009743 

 Test 3 20.32 4 0.0004309 

 Test 4 16.02 4 0.002987 

 Test 5a 13.37 3 0.003903 

 Test 5b 2.656 1 0.1032 

 Test 6a 16.02 4 0.002987 

 Test 6b -1.137e-013 0 N/A 

 Test 7a 0.6367 2 0.7273 

 Test 7b 12.73 1 0.0003594 

 Test 7c 15.39 2 0.0004556 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 
 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.318514 0.301226 

 3 0.404537 0.315934 

 4 0.318514 0.288611 

 5 0.632078 0.523231 

 10000

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

10:55 08/23 2010

BMDL BMD

 

Exponential
 10000

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:55 08/23 2010

BMDL BMD

 

Exponential

 10000

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:55 08/23 2010

BMDL BMD

 

Exponential
 10000

 15000

 20000

 25000

 30000

 35000

 40000

 45000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

10:55 08/23 2010

BMDL BMD

 

Exponential
Chlorpyrifos 91107 

Repeat oral (Corn Oil) 

RBC F (PND) 8 

time 

(hrs) 

BMDS Exponential MODEL variance 

Bmr = 10% 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Aug 20 09:21:33 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 1.24044 1.24044 1.24044 

1.24044 
 rho 1.5009 1.5009 1.5009 

1.5009 

 a 2183.33 2183.33 6706.88 

6706.88 

 b 0.618895 0.618895 0.695862 

0.695862 

 c -- -- 0.02156 

0.02156 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 0.132479 0.132478 0.104344 

0.175939 

 rho 1.63673 1.63673 1.64009 

1.63095 

 a 6319.08 6319.08 6336.78 

6231.54 

 b 0.618245 0.618245 0.635255 

0.76121 

 c -- -- 0.00765865 

0.0851656 

 d -- 1 -- 

1.23628 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 5951 2082 

 0.05 8 6388 1021 

 0.1 8 5882 1282 

 0.5 8 4870 829 

 1 8 3333 821 

 3.5 8 723 264.2 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 6319 1377 -0.7558 

 0.05 6127 1343 0.5492 

 0.1 5940 1310 -0.1258 

 0.5 4639 1070 0.6114 

 1 3405 830.5 -0.2462 

 3.5 725.9 234.4 -0.0355 

 3 0 6319 1377 -0.7558 

 0.05 6127 1343 0.5492 

 0.1 5940 1310 -0.1258 

 0.5 4639 1070 0.6114 

 1 3405 830.5 -0.2462 

 3.5 725.9 234.4 -0.0355 

 4 0 6337 1382 -0.7898 

 0.05 6140 1346 0.5196 
 0.1 5950 1312 -0.146 

 0.5 4626 1067 0.6478 

 1 3380 825.2 -0.1613 

 3.5 729.2 234.6 -0.0745 

 5 0 6232 1357 -0.5847 

 0.05 6132 1339 0.5392 

 0.1 6000 1316 -0.2541 

 0.5 4742 1086 0.3344 

 1 3323 812.7 0.03417 

 3.5 729.1 235.9 -0.07263 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -360.5861 7 735.1722 

 A2 -346.6573 12 717.3147 

 A3 -349.8923 8 715.7846 

 R -394.7781 2 793.5561 

 2 -350.0992 4 708.1983 

 3 -350.0992 4 708.1983 

 4 -350.0925 5 710.185 

 5 -349.9949 6 711.9898 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 
 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 96.24 10 < 0.0001 

 Test 2 27.86 5 < 0.0001 

 Test 3 6.47 4 0.1667 

 Test 4 0.4137 4 0.9813 

 Test 5a 0.4137 4 0.9813 

 Test 5b 3.411e-013 0 N/A 

 Test 6a 0.4004 3 0.9402 

 Test 6b 0.01335 1 0.908 

 Test 7a 0.2052 2 0.9025 

 Test 7b 0.2086 2 0.901 

 Test 7c 0.1952 1 0.6586 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 
 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.170419 0.154398 

 3 0.170419 0.154398 

 4 0.167206 0.129809 

 5 0.229627 0.130915 

 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:21 08/20 2010

BMDL BMD

 

Exponential
 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:21 08/20 2010

BMDL BMD

 

Exponential

 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:21 08/20 2010

BMDL BMD

 

Exponential
 0

 1000

 2000

 3000

 4000

 5000

 6000

 7000

 8000

 0 0.5 1 1.5 2 2.5 3 3.5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:21 08/20 2010

BMDL BMD

 

Exponential
48139301 acute oral (milk) Brain F (PND) 8 0 26410.1 U/L 3026.3 8

Wed Jan 26 11:55:42 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 7.886 7.886 1.551 1.551 1.000 1.000 

mu 0.184 0.184 0.933 0.933 1.448 1.448 

lA.sexF 0.000 0.236 0.726 0.000 0.000 0.039 

lA.sexM 0.236 0.000 0.000 0.726 0.039 0.000 

lD.sexF 0.000 0.984 0.002 0.000 0.000 0.014 

lD.sexM 0.984 0.000 0.000 0.002 0.014 0.000 

lg.sexF 0.000 0.978 0.008 0.000 0.000 0.014 

lg.sexM 0.978 0.000 0.000 0.008 0.014 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.001 0.000 -0.008 0.000 -0.006 0.000 

lA.sexM 0.000 0.001 0.000 -0.008 0.000 -0.006 

lD.sexF -0.008 0.000 0.174 0.000 0.144 0.000 

lD.sexM 0.000 -0.008 0.000 0.174 0.000 0.144 

lg.sexF -0.006 0.000 0.144 0.000 0.129 0.000 

lg.sexM 0.000 -0.006 0.000 0.144 0.000 0.129 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.496 0.000 -0.428 0.000 

lA.sexM 0.000 1.000 0.000 -0.496 0.000 -0.428 

lD.sexF -0.496 0.000 1.000 0.000 0.963 0.000 

lD.sexM 0.000 -0.496 0.000 1.000 0.000 0.963 

lg.sexF -0.428 0.000 0.963 0.000 1.000 0.000 

lg.sexM 0.000 -0.428 0.000 0.963 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.595 0.038 

lA.sexM 1.595 0.038 

lD.sexF 0.412 0.417 

lD.sexM 0.412 0.417 

lg.sexF 0.552 0.359 

lg.sexM 0.552 0.359 

---- 

Mean Absolute Value of Gradient 

 Val 
lA.sexF 2.1599569 

lA.sexM 2.1599569 

lD.sexF 0.3735085 

lD.sexM 0.3735086 

lg.sexF 0.3531427 

lg.sexM 0.3531427 

Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.929073 (0.09827216) 

BMD10 estimate (standard error): 1.529981 (0.4643814) 

Power parameter estimate (standard error): 1.767668 (0.521671) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.7400654 4.929073 5.125617 
BMD 0.9520477 1.529981 2.458744 

Power Parameter 1.1115758 1.767668 2.811010 

 BMDL 

1.028805 
91107 acute oral (gavage) brain 

M 

(PND) 8

Exponential model 

Constant variance 

Bmr 10 

Risk – relative 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Actualruns_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Sep 09 12:37:49 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 15.5873 15.5873 15.5873 

15.5873 
 rho(S) 0 0 0 

0 

 a 17425.5 17425.5 27185.6 

27185.6 

 b 0.152906 0.152906 0.167351 

0.167351 

 c -- -- 0.000410619 

0.000410619 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 16.0692 15.7341 16.0692 

15.7311 

 rho 0 0 0 

0 

 a 25353.3 24553.9 25353.3 

24549.1 

 b 0.131784 0.180912 0.131784 

0.438954 

 c -- -- 0 

0.454717 

 d -- 2.35967 -- 

13.1747 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 2.276e+004 3336 

 0.05 8 2.589e+004 2664 

 0.1 8 2.434e+004 1508 

 0.5 8 2.52e+004 1847 

 2 8 2.235e+004 2910 

 5 8 1.116e+004 2821 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2.535e+004 3086 -2.373 

 0.05 2.519e+004 3086 0.6455 

 0.1 2.502e+004 3086 -0.6255 

 0.5 2.374e+004 3086 1.343 

 2 1.948e+004 3086 2.627 

 5 1.312e+004 3086 -1.792 

 3 0 2.455e+004 2610 -1.939 

 0.05 2.455e+004 2610 1.449 

 0.1 2.455e+004 2610 -0.231 

 0.5 2.447e+004 2610 0.7938 

 2 2.242e+004 2610 -0.08304 

 5 1.115e+004 2610 0.01159 
 4 0 2.535e+004 3086 -2.373 

 0.05 2.519e+004 3086 0.6455 

 0.1 2.502e+004 3086 -0.6255 

 0.5 2.374e+004 3086 1.343 

 2 1.948e+004 3086 2.627 

 5 1.312e+004 3086 -1.792 

 5 0 2.455e+004 2606 -1.937 

 0.05 2.455e+004 2606 1.456 

 0.1 2.455e+004 2606 -0.2282 

 0.5 2.455e+004 2606 0.7084 

 2 2.235e+004 2606 -7.294e-009 

 5 1.116e+004 2606 2.892e-009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -398.0957 7 810.1915 

 A2 -394.8779 12 813.7557 

 A3 -398.0957 7 810.1915 

 R -437.7654 2 879.5308 

 2 -409.6608 3 825.3217 

 3 -401.6175 4 811.235 

 4 -409.6608 3 825.3217 

 5 -401.5452 5 813.0904 

 Additive constant for all log-likelihoods = -44.11. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 
 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 85.78 10 < 0.0001 

 Test 2 6.436 5 0.2661 

 Test 3 6.436 5 0.2661 

 Test 4 23.13 4 0.0001193 

 Test 5a 7.044 3 0.07052 

 Test 5b 16.09 1 < 0.0001 

 Test 6a 23.13 4 0.0001193 

 Test 6b 0 0 N/A 

 Test 7a 6.899 2 0.03176 

 Test 7b 0.1446 1 0.7038 

 Test 7c 16.23 2 0.0002988 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 
 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.799495 0.665064 

 3 2.12988 1.52673 

 4 0.799495 0.664911 

 5 2.01815 1.5418 

 10000

 15000

 20000

 25000

 30000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

12:37 09/09 2010

BMDL BMD

 

Exponential
 10000

 15000

 20000

 25000

 30000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:37 09/09 2010

BMDL BMD

 

Exponential

 10000

 15000

 20000

 25000

 30000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:37 09/09 2010

BMDL BMD

 

Exponential
 10000

 15000

 20000

 25000

 30000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

12:37 09/09 2010

BMDL BMD

 

Exponential
Chlorpyrifos 48139301 acute oral (Diet) RBC 

F 

(ADLT) 8

0 5337 U/L 304.8 8 RAT 

R based 

Acute diet 

Female 

Rbc 

Mon Jan 31 10:28:21 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 27.936 27.936 1.526 1.526 1.000 1.000 

mu 0.052 0.052 0.949 0.949 1.448 1.448 

lA.sexF 0.260 0.000 0.000 0.712 0.028 0.000 

lA.sexM 0.000 0.260 0.712 0.000 0.000 0.028 

lD.sexF 0.999 0.000 0.000 0.000 0.001 0.000 

lD.sexM 0.000 0.999 0.000 0.000 0.000 0.001 

lg.sexF 0.999 0.000 0.000 0.000 0.001 0.000 

lg.sexM 0.000 0.999 0.000 0.000 0.000 0.001 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.002 0.000 -0.022 0.000 -0.030 0.000 

lA.sexM 0.000 0.002 0.000 -0.022 0.000 -0.030 

lD.sexF -0.022 0.000 1.004 0.000 1.427 0.000 

lD.sexM 0.000 -0.022 0.000 1.004 0.000 1.427 

lg.sexF -0.030 0.000 1.427 0.000 2.041 0.000 

lg.sexM 0.000 -0.030 0.000 1.427 0.000 2.041 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.516 0.000 -0.500 0.000 

lA.sexM 0.000 1.000 0.000 -0.516 0.000 -0.500 

lD.sexF -0.516 0.000 1.000 0.000 0.997 0.000 

lD.sexM 0.000 -0.516 0.000 1.000 0.000 0.997 

lg.sexF -0.500 0.000 0.997 0.000 1.000 0.000 

lg.sexM 0.000 -0.500 0.000 0.997 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.594 0.042 

lA.sexM 1.594 0.042 

lD.sexF -0.001 1.002 
lD.sexM -0.001 1.002 

lg.sexF 1.027 1.429 

lg.sexM 1.027 1.429 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.8318370 

lA.sexM 1.8318370 

lD.sexF 0.4212766 

lD.sexM 0.4212766 

lg.sexF 0.2934787 

lg.sexM 0.2934787 

Variance is heterogeneous ::: Power parameter fit 
Intercept estimate (standard error): 4.976937 (0.09421813) 

BMD10 estimate (standard error): 0.6631076 (0.08758324) 

Power parameter estimate (standard error): 1.744374 (0.1404777) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.7953746 4.9769365 5.1653728 

BMD 0.5245441 0.6631076 0.8382741 

Power Parameter 1.5023927 1.7443738 2.0253292 

 BMDL 

0.5450309 

> 
Chlorpyrifos 48139301 

Repeat oral (Corn Oil) 

RBC 

F 

(ADLT) 8

0 4903.3 U/L 275.8 8 RAT 

0.05 4654.9 U/L 712.7 8 RAT 

0.1 4119.3 U/L 488.7 8 RAT 

0.5 3946 U/L 722.4 8 RAT 

1 1334.8 U/L 395.1 8 RAT 

3.5 134.5 U/L 184.9 8 RAT 

Mon Jan 31 13:39:43 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 7.750 7.750 1.505 1.505 1.000 1.000 

mu 0.185 0.185 0.953 0.953 1.434 1.434 

lA.sexF 0.000 0.201 0.766 0.000 0.000 0.032 

lA.sexM 0.201 0.000 0.000 0.766 0.032 0.000 

lD.sexF 0.000 0.984 0.001 0.000 0.000 0.015 

lD.sexM 0.984 0.000 0.000 0.001 0.015 0.000 

lg.sexF 0.000 0.979 0.006 0.000 0.000 0.015 

lg.sexM 0.979 0.000 0.000 0.006 0.015 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.002 0.000 -0.005 0.000 -0.006 0.000 

lA.sexM 0.000 0.002 0.000 -0.005 0.000 -0.006 

lD.sexF -0.005 0.000 0.065 0.000 0.080 0.000 

lD.sexM 0.000 -0.005 0.000 0.065 0.000 0.080 

lg.sexF -0.006 0.000 0.080 0.000 0.106 0.000 

lg.sexM 0.000 -0.006 0.000 0.080 0.000 0.106 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.456 0.000 -0.399 0.000 

lA.sexM 0.000 1.000 0.000 -0.456 0.000 -0.399 

lD.sexF -0.456 0.000 1.000 0.000 0.963 0.000 

lD.sexM 0.000 -0.456 0.000 1.000 0.000 0.963 

lg.sexF -0.399 0.000 0.963 0.000 1.000 0.000 

lg.sexM 0.000 -0.399 0.000 0.963 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.538 0.044 

lA.sexM 1.538 0.044 
lD.sexF -0.806 0.255 

lD.sexM -0.806 0.255 

lg.sexF 1.114 0.325 

lg.sexM 1.114 0.325 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.6112950 

lA.sexM 1.6112950 

lD.sexF 0.5774669 

lD.sexM 0.5774669 

lg.sexF 0.3618618 

lg.sexM 0.3618618 
Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.654547 (0.1300956) 

BMD10 estimate (standard error): 0.4465991 (0.08046465) 

Power parameter estimate (standard error): 3.047571 (0.722021) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.4081308 4.6545473 4.9147385 

BMD 0.3282987 0.4465991 0.6075284 

Power Parameter 2.0677843 3.0475705 4.4916126 

 BMDL 

0.3452339 

> 
Chlorpyrifos 48139301 

Repeat oral (Corn Oil) 

Brain F (PND) 8

0 42289.4 U/L 1615.9 8

0.05 44725.8 U/L 2598 8

0.1 43498.6 U/L 1409 8

0.5 42809.6 U/L 1257.7 8

1 34265.8 U/L 2009.1 8

3.5 17344.1 U/L 2413.8 8

R based 

Repeat gavage 

Female pup 

Brain 

Mon Jan 31 11:47:37 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 7.416 7.416 1.565 1.565 1.000 1.000 

mu 0.196 0.196 0.927 0.927 1.450 1.450 

lA.sexF 0.000 0.432 0.000 0.533 0.035 0.000 

lA.sexM 0.432 0.000 0.533 0.000 0.000 0.035 

lD.sexF 0.000 0.983 0.000 0.001 0.016 0.000 

lD.sexM 0.983 0.000 0.001 0.000 0.000 0.016 

lg.sexF 0.000 0.970 0.000 0.014 0.016 0.000 

lg.sexM 0.970 0.000 0.014 0.000 0.000 0.016 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.002 0.000 -0.016 0.000 -0.008 0.000 

lA.sexM 0.000 0.002 0.000 -0.016 0.000 -0.008 

lD.sexF -0.016 0.000 0.276 0.000 0.154 0.000 

lD.sexM 0.000 -0.016 0.000 0.276 0.000 0.154 

lg.sexF -0.008 0.000 0.154 0.000 0.094 0.000 

lg.sexM 0.000 -0.008 0.000 0.154 0.000 0.094 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.657 0.000 -0.584 0.000 

lA.sexM 0.000 1.000 0.000 -0.657 0.000 -0.584 

lD.sexF -0.657 0.000 1.000 0.000 0.956 0.000 

lD.sexM 0.000 -0.657 0.000 1.000 0.000 0.956 

lg.sexF -0.584 0.000 0.956 0.000 1.000 0.000 

lg.sexM 0.000 -0.584 0.000 0.956 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 
 Parms Relative SE 

lA.sexF 1.593 0.045 

lA.sexM 1.593 0.045 

lD.sexF -0.508 0.525 

lD.sexM -0.508 0.525 

lg.sexF 0.222 0.306 

lg.sexM 0.222 0.306 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 2.0950189 

lA.sexM 2.0950189 

lD.sexF 0.3185851 

lD.sexM 0.3185851 

lg.sexF 0.3966182 

lg.sexM 0.3966182 
Variance is homogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.918671 (0.06002615) 

BMD10 estimate (standard error): 0.601496 (0.09747683) 

Power parameter estimate (standard error): 1.248135 (0.1109397) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.8014791 4.918671 5.0387233 

BMD 0.4542628 0.601496 0.7964496 

Power Parameter 1.0597454 1.248135 1.4700145 

 BMDL 

0.4755929 

Chlorpyrifos 48139301 

Repeat oral (Corn Oil) 

RBC F (PND) 8

0 5951 U/L 2082 8 RAT 

R based 

Repeat gavage 

Female pup 

Rbc 

Mon Jan 31 11:40:32 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 6.409 6.409 1.649 1.649 1.000 1.000 

mu 0.229 0.229 0.890 0.890 1.468 1.468 

lA.sexF 0.406 0.000 0.549 0.000 0.000 0.044 

lA.sexM 0.000 0.406 0.000 0.549 0.044 0.000 

lD.sexF 0.978 0.000 0.003 0.000 0.000 0.020 

lD.sexM 0.000 0.978 0.000 0.003 0.020 0.000 

lg.sexF 0.951 0.000 0.028 0.000 0.000 0.021 

lg.sexM 0.000 0.951 0.000 0.028 0.021 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.003 0.000 -0.015 0.000 -0.006 0.000 

lA.sexM 0.000 0.003 0.000 -0.015 0.000 -0.006 

lD.sexF -0.015 0.000 0.223 0.000 0.102 0.000 

lD.sexM 0.000 -0.015 0.000 0.223 0.000 0.102 

lg.sexF -0.006 0.000 0.102 0.000 0.054 0.000 

lg.sexM 0.000 -0.006 0.000 0.102 0.000 0.054 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.639 0.000 -0.528 0.000 

lA.sexM 0.000 1.000 0.000 -0.639 0.000 -0.528 

lD.sexF -0.639 0.000 1.000 0.000 0.935 0.000 

lD.sexM 0.000 -0.639 0.000 1.000 0.000 0.935 

lg.sexF -0.528 0.000 0.935 0.000 1.000 0.000 

lg.sexM 0.000 -0.528 0.000 0.935 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.581 0.050 

lA.sexM 1.581 0.050 

lD.sexF -1.532 0.472 

lD.sexM -1.532 0.472 
lg.sexF 0.103 0.232 

lg.sexM 0.103 0.232 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.7672291 

lA.sexM 1.7672291 

lD.sexF 0.3683798 

lD.sexM 0.3683798 

lg.sexF 0.6145329 

lg.sexM 0.6145329 

Variance is heterogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 4.947321 (0.2076523) 
BMD10 estimate (standard error): 0.1701286 (0.01035339) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.5641790 4.9473206 5.3626253 

BMD 0.1516686 0.1701286 0.1908353 

 BMDL 

0.1545417 
Chlorpyrifos 48139301 

Repeat oral (Corn Oil) 

Brain 

M 

(PND) 8

0 43264.5 U/L 1500.4 8 RAT 

0.05 43558.4 U/L 1981.1 8 RAT 

0.1 42656.8 U/L 1575.7 8 RAT 

0.5 40779.8 U/L 1864.7 8 RAT 

1 31005.9 U/L 4154.3 8 RAT 

3.5 13898 U/L 987.9 8 RAT 

R based 

Repeat gavage 

Male pup 

Brain 

Mon Jan 31 11:58:46 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 7.047 7.047 1.590 1.590 1.000 1.000 

mu 0.207 0.207 0.915 0.915 1.456 1.456 

lA.sexF 0.448 0.000 0.515 0.000 0.000 0.037 

lA.sexM 0.000 0.448 0.000 0.515 0.037 0.000 

lD.sexF 0.981 0.000 0.002 0.000 0.000 0.017 

lD.sexM 0.000 0.981 0.000 0.002 0.017 0.000 

lg.sexF 0.964 0.000 0.019 0.000 0.000 0.018 

lg.sexM 0.000 0.964 0.000 0.019 0.018 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.002 0.000 -0.016 0.000 -0.007 0.000 

lA.sexM 0.000 0.002 0.000 -0.016 0.000 -0.007 

lD.sexF -0.016 0.000 0.256 0.000 0.122 0.000 

lD.sexM 0.000 -0.016 0.000 0.256 0.000 0.122 

lg.sexF -0.007 0.000 0.122 0.000 0.064 0.000 

lg.sexM 0.000 -0.007 0.000 0.122 0.000 0.064 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.668 0.000 -0.584 0.000 

lA.sexM 0.000 1.000 0.000 -0.668 0.000 -0.584 

lD.sexF -0.668 0.000 1.000 0.000 0.949 0.000 

lD.sexM 0.000 -0.668 0.000 1.000 0.000 0.949 

lg.sexF -0.584 0.000 0.949 0.000 1.000 0.000 

lg.sexM 0.000 -0.584 0.000 0.949 0.000 1.000 
---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.617 0.046 

lA.sexM 1.617 0.046 

lD.sexF -0.894 0.506 

lD.sexM -0.894 0.506 

lg.sexF 0.118 0.253 

lg.sexM 0.118 0.253 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 2.0573971 

lA.sexM 2.0573971 

lD.sexF 0.3422228 

lD.sexM 0.3422228 

lg.sexF 0.4932134 

lg.sexM 0.4932134 

Variance is heterogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 5.112553 (0.06391137) 

BMD10 estimate (standard error): 0.3182407 (0.01109938) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.9878482 5.1125531 5.2403758 

BMD 0.2974895 0.3182407 0.3404395 

 BMDL 

0.3007851 

> 

Chlorpyrifos 48139301 

Repeat oral (Corn Oil) 

RBC 

M 

(PND) 8

0 6410 U/L 1605 8 RAT 

0.05 5516.5 U/L 1179.7 8 RAT 

0.1 5449.8 U/L 1293.6 8 RAT 

0.5 4054 U/L 1111.8 8 RAT 

1 2478 U/L 532.5 8 RAT 

3.5 539.8 U/L 332.2 8 RAT 

R based 

Repeat gavage 

Male pup 

Rbc 

Mon Jan 31 12:06:40 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 8.508 8.508 1.773 1.773 1.000 1.000 

mu 0.176 0.176 0.846 0.846 1.501 1.501 

lA.sexF 0.507 0.000 0.455 0.000 0.000 0.038 

lA.sexM 0.000 0.507 0.000 0.455 0.038 0.000 

lD.sexF 0.987 0.000 0.002 0.000 0.000 0.011 

lD.sexM 0.000 0.987 0.000 0.002 0.011 0.000 

lg.sexF 0.967 0.000 0.021 0.000 0.000 0.012 

lg.sexM 0.000 0.967 0.000 0.021 0.012 0.000 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.004 0.000 -0.031 0.000 -0.009 0.000 

lA.sexM 0.000 0.004 0.000 -0.031 0.000 -0.009 

lD.sexF -0.031 0.000 0.488 0.000 0.161 0.000 

lD.sexM 0.000 -0.031 0.000 0.488 0.000 0.161 

lg.sexF -0.009 0.000 0.161 0.000 0.058 0.000 

lg.sexM 0.000 -0.009 0.000 0.161 0.000 0.058 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.720 0.000 -0.624 0.000 

lA.sexM 0.000 1.000 0.000 -0.720 0.000 -0.624 

lD.sexF -0.720 0.000 1.000 0.000 0.959 0.000 

lD.sexM 0.000 -0.720 0.000 1.000 0.000 0.959 

lg.sexF -0.624 0.000 0.959 0.000 1.000 0.000 

lg.sexM 0.000 -0.624 0.000 0.959 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 
 Parms Relative SE 

lA.sexF 1.584 0.062 

lA.sexM 1.584 0.062 

lD.sexF -2.605 0.699 

lD.sexM -2.605 0.699 

lg.sexF -0.210 0.240 

lg.sexM -0.210 0.240 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.590903 

lA.sexM 1.590903 

lD.sexF 0.352936 

lD.sexM 0.352936 

lg.sexF 0.798925 

lg.sexM 0.798925 

01234

0 2000 4000 6000

Male dose-respose curve for chlorpyrifos

dose

Response

BMD

BMDL

Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.865244 (0.3004799) 

BMD10 estimate (standard error): 0.07694372 (0.05494676) 

Power parameter estimate (standard error): 0.825402 (0.1141079) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.33035411 4.86524372 5.4662034 

BMD 0.03168713 0.07694372 0.1868372 

Power Parameter 0.64661820 0.82540200 1.0536178 

 BMDL 

0.03663169 

> 
Data: ChEDataDNTdams 

filename: dow1 che dam gd20 heart simple (SAP ) 

Parameter: Heart dams GD Day 20 (Dow study) 

Model: R program – Single Sex 

BMR: 10% 

Power parameter: 

Female Dose-Response Curve for Chlorpyrifos 

Tue May 05 10:35:40 2009 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 6.477 6.477 1.790 1.790 1.000 1.000 

mu 0.231 0.231 0.837 0.837 1.499 1.499 

lA.sexF 0.388 0.000 0.563 0.000 0.000 0.049 

lA.sexM 0.000 0.388 0.000 0.563 0.049 0.000 

lD.sexF 0.978 0.000 0.004 0.000 0.000 0.018 
lD.sexM 0.000 0.978 0.000 0.004 0.018 0.000 

lg.sexF 0.942 0.000 0.038 0.000 0.000 0.020 

lg.sexM 0.000 0.942 0.000 0.038 0.020 0.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.654 0.082 

lA.sexM 1.654 0.082 

lD.sexF -1.792 0.732 

lD.sexM -1.792 0.732 

lg.sexF -0.102 0.278 

lg.sexM -0.102 0.278 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.6518257 

lA.sexM 1.6518257 

lD.sexF 0.4108376 

lD.sexM 0.4108376 

lg.sexF 0.8517210 

lg.sexM 0.8517209 
Variance is heterogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 4.802256 (0.3618797) 

BMD10 estimate (standard error): 0.2943337 (0.02629857) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.1732895 4.8022559 5.5260154 

BMD 0.2497107 0.2943337 0.3469309 

 BMDL 

0.2566849 

> 
Data: ChEDataDNTdams filename: dow1 che dams gd20 hindbrain 10bmr simple (SAP ) 

Parameter: Hindbrain dams GD Day 20 

Model: R program – Single Sex 

BMR: 10% 

Power parameter: 

Female Dose-Response Curve for Chlorpyrifos 
Mon May 04 08:50:13 2009 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 10.510 10.510 1.475 1.475 1.000 1.000 

mu 0.136 0.136 0.969 0.969 1.429 1.429 

lA.sexF 0.000 0.446 0.535 0.000 0.000 0.018 

lA.sexM 0.446 0.000 0.000 0.535 0.018 0.000 

lD.sexF 0.000 0.991 0.000 0.000 0.000 0.008 

lD.sexM 0.991 0.000 0.000 0.000 0.008 0.000 

lg.sexF 0.000 0.988 0.003 0.000 0.000 0.009 

lg.sexM 0.988 0.000 0.000 0.003 0.009 0.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.608 0.071 

lA.sexM 1.608 0.071 

lD.sexF 0.095 0.779 

lD.sexM 0.095 0.779 

lg.sexF 0.546 0.512 

lg.sexM 0.546 0.512 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.9609364 

lA.sexM 1.9609364 

lD.sexF 0.4676926 

lD.sexM 0.4676926 

lg.sexF 0.5806155 

lg.sexM 0.5806155 
Variance is homogeneous ::: Power parameter fit 

Intercept estimate (standard error): 4.995013 (0.1009121) 

BMD10 estimate (standard error): 1.099816 (0.2976588) 

Power parameter estimate (standard error): 1.72704 (0.283509) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.8010271 4.995013 5.196837 

BMD 0.7135696 1.099816 1.695134 

Power Parameter 1.3001708 1.727040 2.294058 

 BMDL 

0.7673472 
Chemical: Chlorpyrifos (6/9/09) 

Data: ChEDataDNTdams (original data) in C:\Documents and Settings\jliccion\My 

Documents\Jliccion\Benchmark dose folder\Chlorpyrifos SAP BMD reruns 2009\Runs 

Dow1 study dam ChE DNT GD20 & LD1 sap 4 & 5 & 11 & 12\original 

Run file: chedamsGD20RBCsimple in C:\Documents and Settings\jliccion\My 

Documents\Jliccion\Benchmark dose folder\Chlorpyrifos SAP BMD reruns 2009\Runs 

Dow1 study dam ChE DNT GD20 & LD1 sap 4 & 5 & 11 & 12\Runs 

Parameter: Dams ChE RBC GD20 

Model: R program – Single Sex 

BMR: 10% 

parameter

 

Female Dose-Response Curve to Chlorpyrifos 
Tue Jun 09 13:23:30 2009 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 6.548 6.548 1.639 1.639 1.000 1.000 

mu 0.224 0.224 0.895 0.895 1.466 1.466 

lA.sexF 0.000 0.244 0.000 0.705 0.050 0.000 

lA.sexM 0.244 0.000 0.705 0.000 0.000 0.050 

lD.sexF 0.000 0.977 0.000 0.004 0.019 0.000 

lD.sexM 0.977 0.000 0.004 0.000 0.000 0.019 

lg.sexF 0.000 0.962 0.000 0.019 0.020 0.000 

lg.sexM 0.962 0.000 0.019 0.000 0.000 0.020 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.609 0.089 

lA.sexM 1.609 0.089 

lD.sexF -1.938 0.598 

lD.sexM -1.938 0.598 

lg.sexF 0.370 0.346 

lg.sexM 0.370 0.346 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.2727391 

lA.sexM 1.2727391 

lD.sexF 0.4510209 

lD.sexM 0.4510209 

lg.sexF 0.6437339 

lg.sexM 0.6437339 
Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 5.000469 (0.2984647) 

BMD10 estimate (standard error): 0.1439830 (0.077596) 

Power parameter estimate (standard error): 1.447323 (0.3658707) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.4671984 5.0004688 5.5973981 

BMD 0.0692942 0.1439830 0.2991750 

Power Parameter 0.9613044 1.4473232 2.1790646 

 BMDL 

0.07844108 
> 
chlorpyrifos 44648101 DNT 870.6 forebrain F GD20 D

Exponential 

Constant var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Nov 04 09:45:13 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 8.5137 8.5137 8.5137 

8.5137 
 rho(S) 0 0 0 

0 

 a 704.396 704.396 2448.08 

2448.08 

 b 0.423305 0.423305 0.402297 

0.402297 

 c -- -- 0.000126426 

0.000126426 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 10.8009 8.53737 10.8009 

8.53737 

 rho 0 0 0 

0 

 a 2512.07 2317.51 2512.07 

2317.51 

 b 0.312609 0.282322 0.312609 

0.282322 

 c -- -- 0 

0 

 d -- 2.02847 -- 

2.02847 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2332 85.8 

 0.3 5 2286 62.7 

 1 5 2149 85.9 

 5 5 309.5 79 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2512 221.5 -1.823 

 0.3 2287 221.5 -0.0171 

 1 1838 221.5 3.14 

 5 526.3 221.5 -2.188 

 3 0 2318 71.43 0.438 

 0.3 2302 71.43 -0.5185 

 1 2146 71.43 0.08456 

 5 309.8 71.43 -0.009577 

 4 0 2512 221.5 -1.823 

 0.3 2287 221.5 -0.0171 

 1 1838 221.5 3.14 

 5 526.3 221.5 -2.188 

 5 0 2318 71.43 0.438 

 0.3 2302 71.43 -0.5185 
 1 2146 71.43 0.08456 

 5 309.8 71.43 -0.009577 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -95.13697 5 200.2739 

 A2 -94.83363 8 205.6673 

 A3 -95.13697 5 200.2739 

 R -144.8611 2 293.7222 

 2 -118.0094 3 242.0189 

 3 -95.37373 4 198.7475 

 4 -118.0094 3 242.0189 

 5 -95.37373 4 198.7475 

 Additive constant for all log-likelihoods = -18.38. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 100.1 6 < 0.0001 
 Test 2 0.6067 3 0.8949 

 Test 3 0.6067 3 0.8949 

 Test 4 45.74 2 < 0.0001 

 Test 5a 0.4735 1 0.4914 

 Test 5b 45.27 1 < 0.0001 

 Test 6a 45.74 2 < 0.0001 

 Test 6b -2.842e-014 0 N/A 

 Test 7a 0.4735 1 0.4914 

 Test 7b 0 0 N/A 

 Test 7c 45.27 1 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.337036 0.285382 

 3 1.16802 0.975837 

 4 0.337036 0.285382 
 5 1.16802 0.975837 

 0

 500

 1000

 1500

 2000

 2500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

08:45 11/04 2010

BMDL BMD

 

Exponential
 0

 500

 1000

 1500

 2000

 2500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

08:45 11/04 2010

BMDL BMD

 

Exponential

 0

 500

 1000

 1500

 2000

 2500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

08:45 11/04 2010

BMDL BMD

 

Exponential
 0

 500

 1000

 1500

 2000

 2500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

08:45 11/04 2010

BMDL BMD

 

Exponential
chlorpyrifos 44648101 DNT 870.6 heart F GD20 D

Dow1 dnt study 

Exponential 

Constant var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Nov 09 09:54:50 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 8.05686 8.05686 8.05686 

8.05686 

 rho(S) 0 0 0 

0 

 a 139.151 139.151 471.765 

471.765 

 b 0.353526 0.353526 0.939432 

0.939432 

 c -- -- 0.152416 

0.152416 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 8.49206 8.49206 8.38143 

8.05695 

 rho 0 0 0 

0 

 a 466.093 466.093 478.237 

448.55 

 b 0.522439 0.522439 0.751646 

0.990444 

 c -- -- 0.128482 

0.16832 

 d -- 1 -- 

12.2926 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 447.8 78.5 

 0.3 5 449.3 90.2 

 1 5 228.9 33.2 

 5 5 75.5 19.4 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 466.1 69.83 -0.5858 

 0.3 398.5 69.83 1.627 

 1 276.4 69.83 -1.522 

 5 34.2 69.83 1.323 

 3 0 466.1 69.83 -0.5858 

 0.3 398.5 69.83 1.627 

 1 276.4 69.83 -1.522 
 5 34.2 69.83 1.323 

 4 0 478.2 66.07 -1.03 

 0.3 394.1 66.07 1.868 

 1 258 66.07 -0.9849 

 5 71.17 66.07 0.1467 

 5 0 448.6 56.18 -0.02986 

 0.3 448.5 56.18 0.02986 

 1 228.9 56.18 -2.243e-008 

 5 75.5 56.18 5.71e-009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -90.56864 5 191.1373 

 A2 -84.43332 8 184.8666 

 A3 -90.56864 5 191.1373 

 R -112.4185 2 228.837 

 2 -94.92058 3 195.8412 

 3 -94.92058 3 195.8412 

 4 -93.8143 4 195.6286 

 5 -90.56953 5 191.1391 

 Additive constant for all log-likelihoods = -18.38. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 
 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 55.97 6 < 0.0001 

 Test 2 12.27 3 0.006511 

 Test 3 12.27 3 0.006511 

 Test 4 8.704 2 0.01288 

 Test 5a 8.704 2 0.01288 

 Test 5b -3.979e-013 0 N/A 

 Test 6a 6.491 1 0.01084 

 Test 6b 2.213 1 0.1369 

 Test 7a 0.001783 0 N/A 

 Test 7b 8.702 2 0.01289 

 Test 7c 6.49 1 0.01085 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 
 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.20167 0.13615 

 3 0.20167 0.13615 

 4 0.162146 0.115278 

 5 0.854222 0.257375 

 0

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:54 11/09 2010

BMDL BMD

 

Exponential
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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:54 11/09 2010

BMDL BMD

 

Exponential

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:54 11/09 2010

BMDL BMD

 

Exponential
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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:54 11/09 2010

BMDL BMD

 

Exponential
chlorpyrifos 44648101 DNT 870.6 heart F GD20 D

Dow1 dnt study 

Exponential 

Model var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Nov 09 09:58:22 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 
 -------- ------- ------- ------- --

----- 

 lnalpha -1.4951 -1.4951 -1.4951 

-1.4951 

 rho 1.66806 1.66806 1.66806 

1.66806 

 a 139.151 139.151 471.765 

471.765 

 b 0.353526 0.353526 0.939432 

0.939432 

 c -- -- 0.152416 

0.152416 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha -2.28051 -2.28051 -2.60713 -

1.693 

 rho 1.89039 1.89039 1.89043 

1.67257 

 a 431.455 431.455 497.51 

449.824 

 b 0.357413 0.357413 0.936583 

0.992581 

 c -- -- 0.144131 

0.168922 

 d -- 1 -- 

12.4899 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 447.8 78.5 

 0.3 5 449.3 90.2 

 1 5 228.9 33.2 

 5 5 75.5 19.4 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 431.5 98.93 0.3695 

 0.3 387.6 89.39 1.544 

 1 301.8 70.57 -2.31 

 5 72.25 18.27 0.3981 

 3 0 431.5 98.93 0.3695 

 0.3 387.6 89.39 1.544 

 1 301.8 70.57 -2.31 

 5 72.25 18.27 0.3981 

 4 0 497.5 96.14 -1.156 

 0.3 393.2 76.97 1.629 

 1 238.6 48 -0.4521 

 5 75.65 16.21 -0.02014 
 5 0 449.8 70.97 -0.06376 

 0.3 449.8 70.97 -0.01649 

 1 226.3 39.95 0.1463 

 5 75.98 16.04 -0.0676 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -90.56864 5 191.1373 

 A2 -84.43332 8 184.8666 

 A3 -84.93123 6 181.8625 

 R -112.4185 2 228.837 

 2 -91.24629 4 190.4926 

 3 -91.24629 4 190.4926 

 4 -87.8305 5 185.661 

 5 -84.93594 6 181.8719 

 Additive constant for all log-likelihoods = -18.38. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 
 -------- ------------------------ ------ -------------- 

 Test 1 55.97 6 < 0.0001 

 Test 2 12.27 3 0.006511 

 Test 3 0.9958 2 0.6078 

 Test 4 12.63 2 0.001809 

 Test 5a 12.63 2 0.001809 

 Test 5b -5.684e-014 0 N/A 

 Test 6a 5.799 1 0.01604 

 Test 6b 6.832 1 0.008956 

 Test 7a 0.009428 0 N/A 

 Test 7b 12.62 2 0.001817 

 Test 7c 5.789 1 0.01613 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.294787 0.244183 
 3 0.294787 0.244183 

 4 0.132662 0.0982669 

 5 0.854689 0.219895 

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Exponential Model 2 with 0.95 Confidence Level

09:58 11/09 2010

BMDL BMD

 

Exponential
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Exponential Model 3 with 0.95 Confidence Level

09:58 11/09 2010

BMDL BMD

 

Exponential

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:58 11/09 2010

BMDL BMD

 

Exponential
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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:58 11/09 2010

BMDL BMD

 

Exponential
Dow1 dnt study

chlorpyrifos 44648101 DNT 870.63 hindbrain F GD20 D

Exponential

Constant var

10 bmr

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Nov 04 09:07:16 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 8.33317 8.33317 8.33317 8.33317 

 rho(S) 0 0 0 0 
 a 549.268 549.268 1361.22 1361.22 

 b 0.300404 0.300404 0.291204 0.291204 

 c -- -- 0.000225974 

0.000225974 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 9.32203 8.35745 9.32203 8.33863 

 rho 0 0 0 0 

 a 1375.84 1295.11 1375.84 1289.65 

 b 0.258137 0.246907 0.258137 0.85294 

 c -- -- 0 0.238514 

 d -- 1.72667 -- 13.4568 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 1283 87.7 

 0.3 5 1296 92.2 

 1 5 1181 27.6 

 5 5 307.6 62 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1376 105.7 -1.965 

 0.3 1273 105.7 0.4882 

 1 1063 105.7 2.493 

 5 378.5 105.7 -1.499 

 3 0 1295 65.28 -0.4182 

 0.3 1281 65.28 0.5372 

 1 1184 65.28 -0.127 

 5 307.2 65.28 0.01301 

 4 0 1376 105.7 -1.965 

 0.3 1273 105.7 0.4882 

 1 1063 105.7 2.493 

 5 378.5 105.7 -1.499 

 5 0 1290 64.67 -0.2334 

 0.3 1290 64.67 0.2334 

 1 1181 64.67 -2.186e-008 

 5 307.6 64.67 3.986e-009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 
 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -93.33169 5 196.6634 

 A2 -89.98273 8 195.9655 

 A3 -93.33169 5 196.6634 

 R -130.6602 2 265.3204 

 2 -103.2203 3 212.4407 

 3 -93.5745 4 195.149 

 4 -103.2203 3 212.4407 

 5 -93.38631 5 196.7726 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 81.35 6 < 0.0001 

 Test 2 6.698 3 0.08218 

 Test 3 6.698 3 0.08218 

 Test 4 19.78 2 < 0.0001 

 Test 5a 0.4856 1 0.4859 

 Test 5b 19.29 1 < 0.0001 

 Test 6a 19.78 2 < 0.0001 

 Test 6b 0 0 N/A 

 Test 7a 0.1092 0 N/A 

 Test 7b 0.3764 1 0.5395 

 Test 7c 19.67 2 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 
 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.408157 0.351427 

 3 1.10015 0.804926 

 4 0.408157 0.35139 

 5 1.01347 0.823914 
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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

08:07 11/04 2010

BMDL BMD

 

Exponential
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dose

Exponential Model 3 with 0.95 Confidence Level

08:07 11/04 2010

BMDL BMD

 

Exponential
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dose

Exponential Model 4 with 0.95 Confidence Level

08:07 11/04 2010

BMDL BMD

 

Exponential
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dose

Exponential Model 5 with 0.95 Confidence Level

08:07 11/04 2010

BMDL BMD

 

Exponential
chlorpyrifos 44648101 DNT 870.63 RBC F GD20 D

Dow1 dnt study 

Exponential 

Constant var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Nov 09 09:26:03 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 7.00652 7.00652 7.00652 

7.00652 
 rho(S) 0 0 0 

0 

 a 47.1399 47.1399 373.485 

373.485 

 b 0.560906 0.560906 1.24423 

1.24423 

 c -- -- 0.0446247 

0.0446247 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 7.30274 7.06976 7.28968 

7.00652 

 rho 0 0 0 

0 

 a 365.259 355.7 365.469 

355.7 

 b 1.45943 1.46577 1.52086 

1.58615 

 c -- -- 0.0235257 

0.0491988 

 d -- 1.4469 -- 

1.52088 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 355.7 46.8 

 0.3 4 262.3 51.6 

 1 4 62.5 24 

 5 4 17.5 9.8 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 365.3 38.53 -0.5548 

 0.3 235.8 38.53 1.378 

 1 84.87 38.53 -1.161 

 5 0.2474 38.53 0.8956 

 3 0 355.7 34.29 -1.017e-006 

 0.3 262.3 34.29 1.911e-006 

 1 62.5 34.29 -2.432e-006 

 5 6.3e-006 34.29 1.021 

 4 0 365.5 38.28 -0.5707 

 0.3 234.7 38.28 1.441 

 1 86.58 38.28 -1.258 

 5 8.776 38.28 0.4559 

 5 0 355.7 33.22 -2.305e-007 

 0.3 262.3 33.22 -3.354e-008 
 1 62.5 33.22 7.06e-008 

 5 17.5 33.22 -1.161e-007 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -68.05544 5 146.1109 

 A2 -63.0613 8 142.1226 

 A3 -68.05544 5 146.1109 

 R -93.17983 2 190.3597 

 2 -70.57327 3 147.1465 

 3 -68.59298 4 145.186 

 4 -70.46232 4 148.9246 

 5 -68.05544 5 146.1109 

 Additive constant for all log-likelihoods = -15.62. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 60.24 6 < 0.0001 
 Test 2 9.988 3 0.01867 

 Test 3 9.988 3 0.01867 

 Test 4 5.036 2 0.08063 

 Test 5a 1.075 1 0.2998 

 Test 5b 3.961 1 0.04658 

 Test 6a 4.814 1 0.02823 

 Test 6b 0.2219 1 0.6376 

 Test 7a 1.421e-013 0 N/A 

 Test 7b 1.075 1 0.2998 

 Test 7c 4.814 1 0.02823 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0721929 0.058417 

 3 0.144037 0.0822147 

 4 0.0710395 0.0566525 
 5 0.148684 0.0879464 

 0

 50

 100

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 350

 400

 450

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:26 11/09 2010

BMDLBMD

 

Exponential
 0

 50

 100

 150

 200

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 300

 350

 400

 450

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:26 11/09 2010

BMDL BMD

 

Exponential

 0

 50

 100

 150

 200

 250

 300

 350

 400

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:26 11/09 2010

BMDLBMD

 

Exponential
 0

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 200

 250

 300

 350

 400

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:26 11/09 2010

BMDL BMD

 

Exponential
chlorpyrifos 44648101 DNT 871 forebrain F 1 D

Dow1 dnt study 

Exponential 

Constant var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Nov 09 11:43:06 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 9.20255 9.20255 9.20255 

9.20255 
 rho(S) 0 0 0 

0 

 a 598.964 598.964 2348.33 

2348.33 

 b 0.465332 0.465332 0.441469 

0.441469 

 c -- -- 0.00010318 

0.00010318 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 10.9723 9.25474 10.9723 

9.23247 

 rho 0 0 0 

0 

 a 2405.58 2217 2405.58 

2211.95 

 b 0.324417 0.295016 0.324417 

0.876479 

 c -- -- 0 

0.109541 

 d -- 2.04798 -- 

18 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2187 175.6 

 0.3 5 2237 86.2 

 1 5 2037 70.1 

 5 5 242.3 80.2 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2406 241.3 -2.022 

 0.3 2182 241.3 0.5005 

 1 1739 241.3 2.757 

 5 475.1 241.3 -2.157 

 3 0 2217 102.2 -0.6473 

 0.3 2202 102.2 0.7634 

 1 2042 102.2 -0.1222 

 5 241.6 102.2 0.01637 

 4 0 2406 241.3 -2.022 

 0.3 2182 241.3 0.5005 

 1 1739 241.3 2.757 

 5 475.1 241.3 -2.157 

 5 0 2212 101.1 -0.5429 

 0.3 2212 101.1 0.5429 
 1 2037 101.1 -2.808e-009 

 5 242.3 101.1 -2.859e-009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -102.0255 5 214.0509 

 A2 -99.06519 8 214.1304 

 A3 -102.0255 5 214.0509 

 R -144.5919 2 293.1837 

 2 -119.7229 3 245.4457 

 3 -102.5474 4 213.0947 

 4 -119.7229 3 245.4457 

 5 -102.3247 5 214.6493 

 Additive constant for all log-likelihoods = -18.38. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 91.05 6 < 0.0001 
 Test 2 5.921 3 0.1155 

 Test 3 5.921 3 0.1155 

 Test 4 35.39 2 < 0.0001 

 Test 5a 1.044 1 0.3069 

 Test 5b 34.35 1 < 0.0001 

 Test 6a 35.39 2 < 0.0001 

 Test 6b -2.842e-014 0 N/A 

 Test 7a 0.5984 0 N/A 

 Test 7b 0.4454 1 0.5045 

 Test 7c 34.8 2 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.324769 0.269365 

 3 1.12964 0.885242 

 4 0.324769 0.269365 
 5 1.01374 0.904149 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

11:43 11/09 2010

BMDL BMD

 

Exponential
 0

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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

11:43 11/09 2010

BMDL BMD

 

Exponential

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

11:43 11/09 2010

BMDL BMD

 

Exponential
 0

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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

11:43 11/09 2010

BMDL BMD

 

Exponential
chlorpyrifos 44648101 DNT 870.63 hindbrain F LD1 D

Dow1 dnt study 

Exponential 

model var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Nov 04 14:19:02 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 
 -------- ------- ------- ------- --

----- 

 lnalpha 2.83544 2.83544 2.83544 

2.83544 

 rho 0.938555 0.938555 0.938555 

0.938555 

 a 440.493 440.493 1226.71 

1226.71 

 b 0.338565 0.338565 0.328513 

0.328513 

 c -- -- 0.000185862 

0.000185862 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha -1.44322 0.239702 -1.44322 

0.239702 

 rho 1.69573 1.36946 1.69573 

1.36946 

 a 1279.98 1141.14 1279.98 

1141.14 

 b 0.334477 0.252108 0.334477 

0.252108 

 c -- -- 0 

0 

 d -- 2.0556 -- 

2.0556 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 1168 100 

 0.3 5 1095 235.3 

 1 5 1089 58.6 

 5 5 228 52.6 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1280 209.5 -1.192 

 0.3 1158 192.4 -0.7251 

 1 916.1 157.7 2.451 

 5 240.4 50.73 -0.5456 

 3 0 1141 139.8 0.4344 

 0.3 1136 139.3 -0.6436 

 1 1076 134.3 0.2181 

 5 228.2 46.44 -0.009721 

 4 0 1280 209.5 -1.192 

 0.3 1158 192.4 -0.7251 

 1 916.1 157.7 2.451 

 5 240.4 50.73 -0.5456 
 5 0 1141 139.8 0.4344 

 0.3 1136 139.3 -0.6436 

 1 1076 134.3 0.2181 

 5 228.2 46.44 -0.009721 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -105.6895 5 221.379 

 A2 -98.26598 8 212.532 

 A3 -102.9408 6 217.8816 

 R -130.0553 2 264.1107 

 2 -107.9575 4 223.915 

 3 -103.0751 5 216.1503 

 4 -107.9575 4 223.915 

 5 -103.0751 5 216.1503 

 Additive constant for all log-likelihoods = -18.38. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 
 -------- ------------------------ ------ -------------- 

 Test 1 63.58 6 < 0.0001 

 Test 2 14.85 3 0.001952 

 Test 3 9.35 2 0.009327 

 Test 4 10.03 2 0.006626 

 Test 5a 0.2687 1 0.6042 

 Test 5b 9.765 1 0.001779 

 Test 6a 10.03 2 0.006626 

 Test 6b 2.842e-014 0 N/A 

 Test 7a 0.2687 1 0.6042 

 Test 7b 5.684e-014 0 N/A 

 Test 7c 9.765 1 0.001779 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.315 0.282275 
 3 1.3273 0.645539 

 4 0.315 0.267246 

 5 1.3273 0.645539 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

13:19 11/04 2010

BMDL BMD

 

Exponential
 200

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

13:19 11/04 2010

BMDL BMD

 

Exponential

 200

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

13:19 11/04 2010

BMDL BMD

 

Exponential
 200

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

13:19 11/04 2010

BMDL BMD

 

Exponential
chlorpyrifos 44648101 DNT 870.6 RBC F LD1 D

Dow1 dnt study 

Exponential 

model var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Nov 09 10:29:04 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 
 lnalpha 1.66224 1.66224 1.66224 

1.66224 

 rho 1.06602 1.06602 1.06602 

1.06602 

 a 19.4368 19.4368 427.875 

427.875 

 b 0.82397 0.82397 1.52843 

1.52843 

 c -- -- 0.0109066 

0.0109066 

 d -- 1 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 5.32675 5.70122 1.3235 

1.32448 

 rho 0.413499 0.339036 1.13547 

1.11352 

 a 414.518 411.109 424.962 

409.84 

 b 1.90988 1.87395 2.13123 

1.92911 

 c -- -- 0.0128033 

0.0137629 

 d -- 1.08687 -- 

1.20447 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 407.5 77.2 

 0.3 5 247.9 37.6 

 1 5 51.8 13.3 

 5 5 4.9 6.4 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 414.5 49.87 -0.3146 

 0.3 233.7 44.3 0.7154 

 1 61.39 33.6 -0.6381 

 5 0.02953 6.925 1.573 

 3 0 411.1 47.99 -0.1682 

 0.3 240.8 43.83 0.3605 

 1 56.82 34.31 -0.3269 

 5 0.004695 6.971 1.57 

 4 0 425 60.2 -0.6486 

 0.3 226.8 42.15 1.12 

 1 55.23 18.9 -0.4063 

 5 5.451 5.076 -0.2426 

 5 0 409.8 55.23 -0.09474 

 0.3 246.5 41.62 0.07273 
 1 50.14 17.15 0.2168 

 5 5.641 5.081 -0.326 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -83.2534 5 176.5068 

 A2 -69.85589 8 155.7118 

 A3 -71.03719 6 154.0744 

 R -112.1756 2 228.3513 

 2 -75.75129 4 159.5026 

 3 -75.64199 5 161.284 

 4 -72.01299 5 154.026 

 5 -71.03719 6 154.0744 

 Additive constant for all log-likelihoods = -18.38. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 84.64 6 < 0.0001 
 Test 2 26.8 3 < 0.0001 

 Test 3 2.363 2 0.3069 

 Test 4 9.428 2 0.008968 

 Test 5a 9.21 1 0.002407 

 Test 5b 0.2186 1 0.6401 

 Test 6a 1.952 1 0.1624 

 Test 6b 7.477 1 0.006251 

 Test 7a -5.684e-014 0 N/A 

 Test 7b 9.21 1 0.002407 

 Test 7c 1.952 1 0.1624 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0551661 0.0458563 

 3 0.0673031 0.0462385 

 4 0.0501131 0.043563 
 5 0.0810027 0.0465544 

 0

 100

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 300

 400

 500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

10:29 11/09 2010

BMDLBMD

 

Exponential
 0

 100

 200

 300

 400

 500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:29 11/09 2010

BMDLBMD

 

Exponential

 0

 100

 200

 300

 400

 500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:29 11/09 2010

BMDLBMD

 

Exponential
 0

 100

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 300

 400

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

10:29 11/09 2010

BMDL BMD

 

Exponential
Chemical: Chlorpyrifos (4/13/09) 

Data: Dow2 study filename: dow2dntfemalebrainsimple (run file) file: 

ChEDataDNT2dams (original data file) 

Parameter: Female Brain GD6-20 

Sex: Female 

Model: R program – Single Sex 

BMR: 10% 

Power parameter: 1.5070456 

 Female Dose-Response Curve for Chlorpyrifos 
Mon Apr 13 14:05:40 2009 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 5.404 5.404 1.479 1.479 1.000 1.000 

mu 0.262 0.262 0.959 0.959 1.418 1.418 

lA.sexF 0.474 0.000 0.000 0.493 0.033 0.000 

lA.sexM 0.000 0.474 0.493 0.000 0.000 0.033 

lD.sexF 0.969 0.000 0.000 0.001 0.031 0.000 

lD.sexM 0.000 0.969 0.001 0.000 0.000 0.031 

lg.sexF 0.940 0.000 0.000 0.029 0.031 0.000 

lg.sexM 0.000 0.940 0.029 0.000 0.000 0.031 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.620 0.074 

lA.sexM 1.620 0.074 

lD.sexF -0.431 0.574 

lD.sexM -0.431 0.574 

lg.sexF 0.415 0.316 

lg.sexM 0.415 0.316 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.8215789 

lA.sexM 1.8215789 

lD.sexF 0.4067996 

lD.sexM 0.4067996 

lg.sexF 0.5373740 

lg.sexM 0.5373740 
Variance is heterogeneous ::: Power parameter fit 

Intercept estimate (standard error): 5.063403 (0.09803422) 

BMD10 estimate (standard error): 0.6455593 (0.1037286) 

Power parameter estimate (standard error): 1.507046 (0.1094634) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.8746436 5.0634028 5.2594713 

BMD 0.4885566 0.6455593 0.8530165 

Power Parameter 1.3158881 1.5070456 1.7259724 

 BMDL 

0.5119655 
Chemical: Chlorpyrifos (4/21/09) 

Data: Dow2 study filename: dow2dntfemalerbcsimple (run file) file: 

ChEDataDNT2dams (original data file) 

Parameter: Female RBC GD6-20 

Sex: Female 

Model: R program – Single Sex 

BMR: 10% 

Power parameter: 

 Female Dose-Response Curve for Chlorpyrifos 
Tue Apr 21 08:45:42 2009 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 8.365 8.365 1.822 1.822 1.000 1.000 

mu 0.181 0.181 0.829 0.829 1.510 1.510 

lA.sexF 0.000 0.151 0.012 0.778 0.059 0.001 

lA.sexM 0.151 0.000 0.778 0.012 0.001 0.059 

lD.sexF 0.000 0.983 0.000 0.006 0.011 0.000 

lD.sexM 0.983 0.000 0.006 0.000 0.000 0.011 

lg.sexF 0.000 0.974 0.000 0.014 0.011 0.000 

lg.sexM 0.974 0.000 0.014 0.000 0.000 0.011 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.610 0.100 

lA.sexM 1.610 0.100 

lD.sexF -2.760 0.817 

lD.sexM -2.760 0.817 

lg.sexF 0.039 0.353 

lg.sexM 0.039 0.353 

---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.0170328 

lA.sexM 1.0170328 

lD.sexF 0.4122730 

lD.sexM 0.4122730 

lg.sexF 0.8811787 

lg.sexM 0.8811787 
Variance is heterogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 5.049589 (1.360545) 

BMD10 estimate (standard error): 0.05605487 (0.01824432) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 3.28135405 5.04958947 7.77068046 

BMD 0.03395319 0.05605487 0.09254351 

 BMDL 

0.03693596 

> 
chlorpyrifos 44556901 DNT 8706300 BRAIN F GD20

Exponential 

Constant var 

10 bmr 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Wed Nov 03 12:57:52 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 12.1573 12.1573 12.1573 

12.1573 
 rho(S) 0 0 0 

0 

 a 2864.41 2864.41 11860.8 

11860.8 

 b 0.476761 0.476761 0.457395 

0.457395 

 c -- -- 9.6958e-005 

9.6958e-005 

 d -- 1 -- 

1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 13.523 12.2365 13.523 

12.1573 

 rho 0 0 0 

0 

 a 12103 11434.8 12103 

11296 

 b 0.360909 0.348145 0.360909 

0.653324 

 c -- -- 0 

0.101806 

 d -- 1.51419 -- 

3.53297 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 1.13e+004 630 

 0.3 5 1.126e+004 334 

 1 5 9274 633 

 5 5 1150 208 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1.21e+004 863.9 -2.089 

 0.3 1.086e+004 863.9 1.043 

 1 8436 863.9 2.168 

 5 1992 863.9 -2.178 

 3 0 1.143e+004 454.1 -0.6835 

 0.3 1.107e+004 454.1 0.9699 

 1 9340 454.1 -0.3245 

 5 1130 454.1 0.1004 

 4 0 1.21e+004 863.9 -2.089 

 0.3 1.086e+004 863.9 1.043 

 1 8436 863.9 2.168 

 5 1992 863.9 -2.178 

 5 0 1.13e+004 436.4 -1.273e-007 

 0.3 1.126e+004 436.4 -1.915e-008 
 1 9274 436.4 9.442e-008 

 5 1150 436.4 8.077e-008 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -131.5731 5 273.1462 

 A2 -127.9929 8 271.9858 

 A3 -131.5731 5 273.1462 

 R -176.86 2 357.72 

 2 -145.2298 3 296.4596 

 3 -132.3653 4 272.7306 

 4 -145.2298 3 296.4596 

 5 -131.5731 5 273.1462 

 Additive constant for all log-likelihoods = -18.38. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 97.73 6 < 0.0001 
 Test 2 7.16 3 0.06696 

 Test 3 7.16 3 0.06696 

 Test 4 27.31 2 < 0.0001 

 Test 5a 1.584 1 0.2081 

 Test 5b 25.73 1 < 0.0001 

 Test 6a 27.31 2 < 0.0001 

 Test 6b -5.684e-014 0 N/A 

 Test 7a 0 0 N/A 

 Test 7b 1.584 1 0.2081 

 Test 7c 27.31 2 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.291931 0.253693 

 3 0.649826 0.536917 

 4 0.291931 0.253693 
 5 0.835998 0.576174 

 0

 2000

 4000

 6000

 8000

 10000

 12000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

11:57 11/03 2010

BMDL BMD

 

Exponential
 0

 2000

 4000

 6000

 8000

 10000

 12000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

11:57 11/03 2010

BMDL BMD

 

Exponential

 0

 2000

 4000

 6000

 8000

 10000

 12000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

11:57 11/03 2010

BMDL BMD

 

Exponential
 0

 2000

 4000

 6000

 8000

 10000

 12000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

11:57 11/03 2010

BMDL BMD

 

Exponential
Dow 2 study 

Female rbc 

chlorpyrifos 44556901 DNT 8706300 RBC F GD20 D

Exponential 

Constant var 

10 bmr 

Warning: optimum may not have been found form model 1 (BMDL). Bad process 

completion in Optimization routine. 

Warning: optimum may not have been found form model 2 (BMDL). Bad process 

completion in Optimization routine. 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Wed Nov 03 13:19:07 2010 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 
 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 10.5247 10.5247 10.5247 

10.5247 

 rho(S) 0 0 0 

0 

 a 3.51007e-190 0 685.125 

685.125 

 b 152.929 0.270806 136.147 

2.35264 

 c -- -- 0 

0 

 d -- 18 -- 

2 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha 11.7348 11.5027 10.9678 

10.5247 

 rho 0 0 0 

0 

 a 652.498 359.643 652.5 

652.5 

 b 152.929 0.270806 136.147 

1.81593 

 c -- -- 0.247765 

0 

 d -- 18 -- 

1.0391 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 4 652.5 408 

 0.3 5 383.3 210 

 1 5 101.7 88.7 

 5 5 0 1.96 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 652.5 353.3 1.052e-005 

 0.3 7.758e-018 353.3 2.426 

 1 2.504e-064 353.3 0.6436 

 5 0 353.3 0 

 3 0 359.6 314.6 1.862 

 0.3 359.6 314.6 0.1681 
 1 359.6 314.6 -1.833 

 5 8.114e-100 314.6 -5.767e-102 

 4 0 652.5 240.8 2.079e-007 

 0.3 161.7 240.8 2.058 

 1 161.7 240.8 -0.5569 

 5 161.7 240.8 -1.501 

 5 0 652.5 192.9 2.692e-007 

 0.3 383.3 192.9 -7.861e-007 

 1 101.7 192.9 1.542e-006 

 5 0.03282 192.9 -0.0003804 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -109.485 5 228.9701 

 A2 -83.82269 8 183.6454 

 A3 -109.485 5 228.9701 

 R -118.6826 2 241.3652 

 2 -117.4627 3 240.9254 

 3 -116.276 4 240.552 

 4 -113.694 4 235.3879 

 5 -109.485 4 226.9701 

 Additive constant for all log-likelihoods = -17.46. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 
 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 69.72 6 < 0.0001 

 Test 2 51.32 3 < 0.0001 

 Test 3 51.32 3 < 0.0001 

 Test 4 15.96 2 0.000343 

 Test 5a 13.58 1 0.0002284 

 Test 5b 2.373 1 0.1234 

 Test 6a 8.418 1 0.003716 

 Test 6b 7.537 1 0.006043 

 Test 7a 1.447e-007 1 0.9997 

 Test 7b 13.58 0 N/A 

 Test 7c 8.418 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 
 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.000688953 Bad completion 

 3 3.25871 Bad completion 

 4 0.00104772 0.000850368 

 5 0.0631473 0.0310436 

 0

 200

 400

 600

 800

 1000

 1200

 1400

-1 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

12:19 11/03 2010

BMDL BMD

 

Exponential
 0

 200

 400

 600

 800

 1000

 1200

 1400

-1 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:19 11/03 2010

BMDL BMD

 

Exponential

 0

 200

 400

 600

 800

 1000

 1200

 1400

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:19 11/03 2010

BMDLBMD

 

Exponential
 0

 200

 400

 600

 800

 1000

 1200

 1400

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

12:19 11/03 2010

BMDL BMD

 

Exponential
Ginger Moser  

Male PND 17

Brain ChE

EPA BMDS  

Exponential model ‐ Continuous – rel dev  

10 bmr

Constant variance

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Jan 24 09:20:04 2011 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 
 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.14586 -2.14586 -2.14586 -2.14586 

 rho(S) 0 0 0 0 

 a 1.43098 1.45527 4.87714 4.87714 

 b 0.0864062 0.00184562 0.22167 0.22167 

 c -- -- 0.174028 

0.174028 

 d -- 2 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.35768 -1.35768 -1.57278 -1.954 

 rho 0 0 0 0 

 a 4.7949 4.7949 4.91829 4.65164 

 b 0.125487 0.125487 0.188557 0.2373 

 c -- -- 0.151415 0.24986 

 d -- 1 -- 2.3993 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 10 4.645 0.3025 

 0.5 10 4.635 0.2166 

 2 10 4.118 0.5246 

 5 10 1.931 0.4058 

 10 10 1.437 0.4232 

 20 10 0.8912 0.1495 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.795 0.5072 -0.9352 

 0.5 4.503 0.5072 0.8224 

 2 3.731 0.5072 2.414 

 5 2.56 0.5072 -3.921 

 10 1.367 0.5072 0.4365 

 20 0.3898 0.5072 3.126 

 3 0 4.795 0.5072 -0.9352 

 0.5 4.503 0.5072 0.8224 

 2 3.731 0.5072 2.414 

 5 2.56 0.5072 -3.921 

 10 1.367 0.5072 0.4365 

 20 0.3898 0.5072 3.126 

 4 0 4.918 0.4555 -1.898 

 0.5 4.543 0.4555 0.6416 

 2 3.607 0.4555 3.546 

 5 2.37 0.4555 -3.049 

 10 1.378 0.4555 0.4102 
 20 0.8408 0.4555 0.3499 

 5 0 4.652 0.3764 -0.0566 

 0.5 4.631 0.3764 0.03756 

 2 4.114 0.3764 0.03042 

 5 1.935 0.3764 -0.03284 

 10 1.163 0.3764 2.298 

 20 1.162 0.3764 -2.277 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 34.37589 7 -54.75177 

 A2 43.48535 12 -62.9707 

 A3 34.37589 7 -54.75177 

 R -58.16722 2 120.3344 

 2 10.73031 3 -15.46062 

 3 10.73031 3 -15.46062 

 4 17.18333 4 -26.36665 

 5 28.62013 5 -47.24027 

 Additive constant for all log-likelihoods = -55.14. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 
 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 203.3 10 < 0.0001 

 Test 2 18.22 5 0.002684 

 Test 3 18.22 5 0.002684 

 Test 4 47.29 4 < 0.0001 

 Test 5a 47.29 4 < 0.0001 

 Test 5b 0 0 N/A 

 Test 6a 34.39 3 < 0.0001 

 Test 6b 12.91 1 0.0003275 

 Test 7a 11.51 2 0.003165 

 Test 7b 35.78 2 < 0.0001 

 Test 7c 22.87 1 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 
 ------- ------------ ---------- 

 2 0.839612 0.741626 

 3 0.839612 0.741626 

 4 0.664975 0.576235 

 5 1.87391 1.47351 

 0.5

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:20 01/24 2011

BMDL BMD

 

Exponential
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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:20 01/24 2011

BMDL BMD

 

Exponential
 0.5

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:20 01/24 2011

BMDL BMD

 

Exponential
 0.5

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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:20 01/24 2011

BMDL BMD

 

Exponential
Ginger Moser 

Male PND 17 

RBC ChE 

EPA BMDS 

Exponential model - Continuous – rel dev 

10 bmr 

nonhomogeneous variance 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: 

C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Jan 24 09:52:58 2011 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 6 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 
 lnalpha -4.97525 -4.97525 -4.97525 -

4.97525 

 rho 0.755844 0.755844 0.755844 

0.755844 

 a 0.0705752 0.281819 0.76965 

0.76965 

 b 0.167635 0.0102239 0.30979 

0.30979 

 c -- -- 0.0318017 

0.0318017 

 d -- 2 -- 

1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 

Model 5 

 -------- ------- ------- ------- --

----- 

 lnalpha -5.16362 -5.16362 -5.06982 -

5.32654 

 rho 0.280456 0.280456 0.443427 

0.299807 

 a 0.740524 0.740524 0.746358 

0.73489 

 b 0.273522 0.273522 0.301401 

0.313235 

 c -- -- 0.0333737 

0.0533944 

 d -- 1 -- 

1.15886 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 10 0.733 0.04383 

 0.5 10 0.6571 0.06872 

 2 10 0.4398 0.08411 

 5 10 0.1469 0.04648 

 10 10 0.083 0.05824 

 20 10 0.0257 0.01267 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 0.7405 0.07252 -0.3281 

 0.5 0.6459 0.07114 0.4992 

 2 0.4285 0.06716 0.5316 

 5 0.1886 0.05986 -2.204 

 10 0.04805 0.04942 2.237 

 20 0.003117 0.03367 2.121 

 3 0 0.7405 0.07252 -0.3281 

 0.5 0.6459 0.07114 0.4992 

 2 0.4285 0.06716 0.5316 

 5 0.1886 0.05986 -2.204 

 10 0.04805 0.04942 2.237 

 20 0.003117 0.03367 2.121 
 4 0 0.7464 0.07429 -0.5686 

 0.5 0.6454 0.07194 0.513 

 2 0.4197 0.06539 0.9701 

 5 0.1848 0.05451 -2.196 

 10 0.06033 0.04253 1.686 

 20 0.02665 0.03549 -0.08445 

 5 0 0.7349 0.06657 -0.08978 

 0.5 0.6583 0.06548 -0.05741 

 2 0.4281 0.06139 0.6024 

 5 0.1686 0.05339 -1.288 

 10 0.05551 0.0452 1.923 

 20 0.0394 0.04294 -1.009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 145.1405 7 -276.2809 

 A2 158.7751 12 -293.5502 

 A3 150.3958 8 -284.7916 

 R 45.50894 2 -87.01788 

 2 141.8192 4 -275.6385 

 3 141.8192 4 -275.6385 

 4 143.6454 5 -277.2909 

 5 144.0066 6 -276.0133 

 Additive constant for all log-likelihoods = -55.14. This constant added 

to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 
 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 226.5 10 < 0.0001 

 Test 2 27.27 5 < 0.0001 

 Test 3 16.76 4 0.002153 

 Test 4 17.15 4 0.001805 

 Test 5a 17.15 4 0.001805 

 Test 5b -4.945e-012 0 N/A 

 Test 6a 13.5 3 0.00367 

 Test 6b 3.652 1 0.05599 

 Test 7a 12.78 2 0.00168 

 Test 7b 4.375 2 0.1122 

 Test 7c 0.7224 1 0.3954 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 
 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.3852 0.346963 

 3 0.3852 0.346963 

 4 0.362322 0.322049 

 5 0.481397 0.327596 

 0

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:52 01/24 2011

BMDLBMD

 

Exponential
 0

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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:52 01/24 2011

BMDLBMD

 

Exponential

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:52 01/24 2011

BMDLBMD

 

Exponential
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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:52 01/24 2011

BMDL BMD

 

Exponential
Ginger Moser 

Male PND 17 

Brain ChE 

R-based single sex BMD Program 

Mon Jan 24 10:06:19 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 9.122 9.122 1.816 1.816 1.000 1.000 

mu 0.166 0.166 0.832 0.832 1.510 1.510 

lA.sexF 0.000 0.450 0.000 0.509 0.041 0.000 

lA.sexM 0.450 0.000 0.509 0.000 0.000 0.041 

lD.sexF 0.000 0.988 0.000 0.003 0.009 0.000 

lD.sexM 0.988 0.000 0.003 0.000 0.000 0.009 

lg.sexF 0.000 0.973 0.000 0.017 0.010 0.000 

lg.sexM 0.973 0.000 0.017 0.000 0.000 0.010 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.003 0.000 -0.019 0.000 -0.006 0.000 

lA.sexM 0.000 0.003 0.000 -0.019 0.000 -0.006 

lD.sexF -0.019 0.000 0.258 0.000 0.090 0.000 

lD.sexM 0.000 -0.019 0.000 0.258 0.000 0.090 

lg.sexF -0.006 0.000 0.090 0.000 0.034 0.000 

lg.sexM 0.000 -0.006 0.000 0.090 0.000 0.034 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.685 0.000 -0.588 0.000 

lA.sexM 0.000 1.000 0.000 -0.685 0.000 -0.588 

lD.sexF -0.685 0.000 1.000 0.000 0.964 0.000 

lD.sexM 0.000 -0.685 0.000 1.000 0.000 0.964 

lg.sexF -0.588 0.000 0.964 0.000 1.000 0.000 

lg.sexM 0.000 -0.588 0.000 0.964 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.657 0.053 

lA.sexM 1.657 0.053 

lD.sexF -0.448 0.508 

lD.sexM -0.448 0.508 

lg.sexF -0.117 0.184 

lg.sexM -0.117 0.184 
---- 

Mean Absolute Value of Gradient 

 Val 

lA.sexF 1.5677228 

lA.sexM 1.5677228 

lD.sexF 0.4792042 

lD.sexM 0.4792042 

lg.sexF 1.0780830 

lg.sexM 1.0780830 

Variance is homogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 5.162048 (0.1236352) 

BMD10 estimate (standard error): 0.8385296 (0.06262208) 

The confidence intervals for the variables are given by 
 lower est. upper 

Intercept 4.9260808 5.1620479 5.4093182 

BMD 0.7295612 0.8385296 0.9637738 

 BMDL 

0.7462883 
Ginger Moser 

Male PND 17 

RBC ChE 

R-based single sex BMD Program 

Mon Jan 24 10:37:30 2011 

Redundancy Analysis 

 1 2 3 4 5 6 

CondIndex 8.372 8.372 1.745 1.745 1.000 1.000 

mu 0.179 0.179 0.857 0.857 1.495 1.495 

lA.sexF 0.385 0.000 0.000 0.571 0.045 0.000 

lA.sexM 0.000 0.385 0.571 0.000 0.000 0.045 

lD.sexF 0.986 0.000 0.000 0.003 0.011 0.000 

lD.sexM 0.000 0.986 0.003 0.000 0.000 0.011 

lg.sexF 0.971 0.000 0.000 0.017 0.012 0.000 

lg.sexM 0.000 0.971 0.017 0.000 0.000 0.012 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 0.003 0.000 -0.018 0.000 -0.006 0.000 

lA.sexM 0.000 0.003 0.000 -0.018 0.000 -0.006 

lD.sexF -0.018 0.000 0.242 0.000 0.096 0.000 

lD.sexM 0.000 -0.018 0.000 0.242 0.000 0.096 

lg.sexF -0.006 0.000 0.096 0.000 0.041 0.000 

lg.sexM 0.000 -0.006 0.000 0.096 0.000 0.041 

 lA.sexF lA.sexM lD.sexF lD.sexM lg.sexF lg.sexM 

lA.sexF 1.000 0.000 -0.635 0.000 -0.537 0.000 

lA.sexM 0.000 1.000 0.000 -0.635 0.000 -0.537 

lD.sexF -0.635 0.000 1.000 0.000 0.960 0.000 

lD.sexM 0.000 -0.635 0.000 1.000 0.000 0.960 

lg.sexF -0.537 0.000 0.960 0.000 1.000 0.000 

lg.sexM 0.000 -0.537 0.000 0.960 0.000 1.000 

---- 

Parameter Values and Relative Standard Errors (sigma == 1) 

 Parms Relative SE 

lA.sexF 1.619 0.058 

lA.sexM 1.619 0.058 

lD.sexF -0.895 0.492 

lD.sexM -0.895 0.492 

lg.sexF 0.036 0.204 

lg.sexM 0.036 0.204 
Variance is heterogeneous ::: Power parameter set to 1 

Intercept estimate (standard error): 5.052217 (0.1244639) 

BMD10 estimate (standard error): 0.3846067 (0.02402530) 

The confidence intervals for the variables are given by 

 lower est. upper 

Intercept 4.8149786 5.0522175 5.3011454 

BMD 0.3418926 0.3846067 0.4326573 

 BMDL 

0.3485101