DDVP Dog ‐ Week 52 ‐ RBC ChE – Males

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

in Optimization routine. 

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

 Input Data File: 

C:/Users/jliccion/Desktop/BMDS230/Data/exp_TestContinuous1_Setting.(d) 

 Gnuplot Plotting File: 

 Wed May 21 09:58:16 2014 

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 BMDS Model Run 

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 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 -1.87114 -1.87114 -1.87114 -1.87114 

 rho 1.12058 1.12058 1.12058 1.12058 

 a 2.43323 2.43323 11.34 11.34 

 b 0.69011 0.69011 0.948193 0.948193 

 c -- -- 0.0573192 

0.0573192 

 d -- 1 -- 1 
 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

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

 lnalpha -1.77348 -1.87114 -2.20608 -2.20608 

 rho 1.05211 1.12058 1.19831 1.19831 

 a 10.1386 2.43323 10.4076 10.4076 

 b 0.734019 0.69011 1.03775 1.03775 

 c -- -- 0.0850058 0.0850058 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

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

 0 4 10.8 1.31 

 0.05 4 9.5 1.69 

 1 4 4.3 0.8 

 3 4 1.3 0.47 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

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

 2 0 10.14 1.393 0.9493 

 0.05 9.773 1.367 -0.3998 

 1 4.866 0.9471 -1.196 

 3 1.121 0.4375 0.8179 

 3 0 0 0 1.#IO 

 0.05 0 0 1.#IO 

 1 0 0 1.#IO 

 3 0 0 1.#IO 

 4 0 10.41 1.351 0.5811 

 0.05 9.926 1.313 -0.6492 

 1 4.258 0.7906 0.1057 

 3 1.308 0.3898 -0.04134 

 5 0 10.41 1.351 0.5811 

 0.05 9.926 1.313 -0.6492 

 1 4.258 0.7906 0.1057 

 3 1.308 0.3898 -0.04134 

 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 -8.148268 5 26.29654 

 A2 -4.964902 8 25.9298 

 A3 -5.241243 6 22.48249 

 R -30.11069 2 64.22139 

 2 -7.053267 4 22.10653 

 3 -753.94 4 1515.88 

 4 -5.582085 5 21.16417 

 5 -5.582085 5 21.16417 

 Additive constant for all log-likelihoods = -14.7. 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 50.29 6 < 0.0001 

 Test 2 6.367 3 0.09507 

 Test 3 0.5527 2 0.7586 

 Test 4 3.624 2 0.1633 

 Test 5a 1497 2 < 0.0001 

 Test 5b -1494 0 N/A 

 Test 6a 0.6817 1 0.409 

 Test 6b 2.942 1 0.08628 

 Test 7a 0.6817 1 0.409 

 Test 7b 1497 1 < 0.0001 

 Test 7c -3.499e-013 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 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 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 less than .05. Model 5 appears 

 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 0.143539 0.116788 

 3 0.152672 Bad completion 

 4 0.111526 0.0887295 

 5 0.111526 0.0887295 
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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:58 05/21 2014

BMDL BMD

 

Exponential
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Mean Response

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Exponential Model 3 with 0.95 Confidence Level

09:58 05/21 2014

BMDL BMD

 

Exponential
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Mean Response

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Exponential Model 4 with 0.95 Confidence Level

09:58 05/21 2014

BMDL BMD

 

Exponential
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Mean Response

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Exponential Model 5 with 0.95 Confidence Level

09:58 05/21 2014

BMDL BMD

 

Exponential