HDTenEightyP/NetNews huggingface dataset:
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Date:         Thu, 21 Jan 1993 08:25:46 -0600
From:         "J. Philip Miller" <phil@WUBIOS.WUSTL.EDU>
Subject:      Re: Nominal, Ordinal, Interval,
              and Ratio Typologies are Misleading
              Jan 20, 93 10:55:28 pm

> Do you, by any chance, have the complete reference for this article (volume
> and page numbers?)
>
Sorry, I forget that not all subscribers of this list are members of the
American Statistical Association and thus are automatically subscribers to the
American Statistician.  The reference is:


PF Velleman and L Wilkinson.  Nominal, Ordinal, Interval and Ratio Typologies
are Misleading.  The American Statistician, Feb 1993 47(1):65-72.

-phil

--
     J. Philip Miller, Professor, Division of Biostatistics, Box 8067
         Washington University Medical School, St. Louis MO 63110
     phil@wubios.WUstl.edu - Internet  (314) 362-3617 [362-2694(FAX)]
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Date:         Thu, 28 Jan 1993 13:19:48 GMT
From:         Ronan M Conroy <RCONROY@IRLEARN.UCD.IE>
Subject:      Re: correlation coefficients

On Wed, 27 Jan 1993 16:34:00 EST <KIRISCI@PITTVMS> said:
>
>I have two questionnaries:one has items with 7-point scale and the other one
>has
>   items with 3-point scale. I wonder if anyone suggest me how to calculate
>correlation coefficients between individual items whose have different scales.
>What can be the best way? or do I need to make some transformations?
>I am looking forward to reading your comments.
>thank you.

Seems to me that this is a case where Kendall's tau should work better
than Spearman's rho, since one of the scales has few values.
But, apart from the old SPSS manual (V5?) has anyone a reference
on when tau works better than rho?


ronan
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Date:         Thu, 21 Jan 1993 09:45:22 PST
From:         AHK3%MR%MCS@CTS27.CS.PGE.COM
Subject:      re: How to skip variables in SAS

Haiyi Xie (FREUD.SAS.UTAH.EDU) asked for a short cut in selecting just a few
variables from a raw data file which will converted in to a SAS data set for
susequent data analyses.

Haiyi's problem is that there are twelve lines of raw data per observation,
and a total of 177 variables, from which only four or five are to be included
in the SAS data set.  As it turns out, the desired variables are all on the
same line of raw data.

The quickest way to solve the problem is the following SAS data step:

DATA CLASS(KEEP=POP71 POP75-POP77 POP85);
   INFILE SOC631.DAT LRECL=320;
        INPUT   #1
                #2 (POP70-POP85)  (16*20.)
                #3
                #4
                #5
                #6
                #7
                #8
                #9
                #10
                #11
                #12 ;
                        RUN;

This data step will only read the variables in the second line of raw data,
and force the pointer to skip the rest of the lines of raw data.  The KEEP
statement tells SAS to keep only the variables POP71, POP75, POP76 and POP85
in the output SAS data set.

Haiyi should see a significant reduction in data step processing time and
memory requirements as only one line of the raw data is read through the SAS
program data vector and, subsequently, only four variables are included in the
SAS data set.

Andrew Karp
Pacific Gas & Electric Company
AHK3 @DBU.PGE.COM
From: WSIMPSON@UWPG02.BITNET (BILL SIMPSON)
Newsgroups: bit.listserv.stat-l
Subject: r for nonlinear regression
Date: 21 Jan 93 16:50:20 GMT
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I have been using Kaleidagraph on the Mac to do some nonlinear regression.
This is real nonlinear regression with user-specified function (i.e. I am
not talking about polynomial fit or log, exponential, or power fit done by
doing ordinary linear regression on log x &/or y).

The routine returns the estimated parameters for the function and chi-squared
and r.
I asked the company about these things.  The chi-squared is
sum((y-yhat)/s)^2 where s is SD of each point (only makes sense if you
have repeated observations at each x level and you use the mean and SD).
This formula for chi-squared is the same as SSE if sd=1 ie if unweighted least
squares is used.  Does anyone have a reference for this?

I was wondering what an r could possibly mean for a nonlinear fit.  They say
they use:
r=sqrt(1-(chi-squared/sum w(y-ybar)^2)), where w is the weight obtained from
the sd of the observation.  If unweighted, this is the same formula one
might use for linear regression: sqrt(1-(sse/ssto)).  I don't see how it makes
any sense for nonlinear regression.

This can't make sense because it measures degree of fit TO A LINE, not to the
nonlinear regression function.  (It seems to me.) The only r that would
make sense would be a nonparametric r such as Spearman's.

Any help much appreciated.

Bill Simpson
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Date:         Wed, 27 Jan 1993 08:32:00 PST
From:         Charles Chamberlin <CHAMBERLINC@AXE.HUMBOLDT.EDU>
Subject:      RE:testing for periodicity

Data that may contain periodic components are often best characterized
by the use of circular distributions.  These are suitable for interval
scales of measurement where there is no non-arbitrary zero point and no
non-arbitrary high/low values.  Observations with respect to direction,
time-of-day, day-of-year, and day-of-lunar_cycle ( and in some cultural
settings day-of-week) are commonly described using circular distributions.
Zar[, J.H. (1984) Biostatistical Analysis, 2nd ed., Prentice-Hall]
provides a good introduction to the use of curcular distributions in
chapters 24 and 25.
---------------------------------------------------------------------
Charles E. Chamberlin                   (707) 826-4346
Environmental Resources Enginering      (707) 826-3616 (fax)
Humboldt State University               InterNET:
Arcata, California 95521                  chamberlinc@axe.humboldt.edu
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Date:         Thu, 28 Jan 1993 10:52:00 LCL
From:         CROSS <CROSS.CPC@MHS.UNC.EDU>
Subject:      tobit analysis

Another categorical data analysis question for you folks.  What software do
you use for tobit analysis and why?  Will summarize responses.

Thanks!

Catherine Cross
CROSS.CPC@MHS.UNC.EDU
Carolina Population Center
CB#8120, University Square East
University of North Carolina
Chapel Hill, NC  27516-3997
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Date:         Fri, 22 Jan 1993 09:09:00 PST
From:         EDF@MSMAIL.HUMAN.CORNELL.EDU
Subject:      Fraility models in survival analysis

Is anyone aware of the existence of software to estimate fraility models in
survival analysis?  A fraility model adds a parameter to the Cox proportional
hazards model to account for heterogeneity not explained by covariates.
..............................................................................
Edward A. Frongillo, Jr.
Office of Computing and Statistical Consulting
College of Human Ecology and Division of Nutritional Sciences
Cornell University
B36 Savage Hall
Ithaca, New York  14853-6301
edf@msmail.human.cornell.edu
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Date:         Mon, 25 Jan 1993 17:23:19 -0800
From:         JAMES@UCRAC1.UCR.EDU
Subject:      RE: Violated t-test assumptions: how to adjust?

I wonder, is it feasible to apply a 'variance stabilising' transformation to
thy data before analysis, e.g. logs? what sort of data do you have %, counts,
etc?
james ssemakula
u.c. riverside
From: KATTAN@UHUPVM1.UH.EDU (Mike Kattan)
Newsgroups: bit.listserv.stat-l
Subject: Measurement Error vs. Bias
Date: Thu, 28 Jan 93 07:55:55 CDT
Organization: University of Houston
NNTP-Posting-Host: uhupvm1.uh.edu

I'd greatly appreciate feedback concerning the following arguments
concerning the terms "measurement error" and "bias".  In particular,
references supporting or refuting these arguments would be helpful.
 
Say one is following the typical steps in prediction (1) determining a
variable list, (2) collecting a sample, (3) modeling the sample, and (4)
applying the model to new data.  In the sample of step (2), the values
of the predictor variables do not correspond appropriately with the
values of the response variable.  This would be called measurement
error, which may occur due to misunderstood questions, approximations,
incorrect observations, etc.  If the values of the response variable are
inappropriate (e.g., sample mean does not equal population mean), bias
has occurred.  Another example of bias is if the observed distribution
of the response variable does not follow the true distribution of the
response variable.
 
[So basically the argument here is that X problems are due to
measurement error, and Y problems are due to bias -- is this a
reasonable interpretation of the terms "measurement error" and
"bias"?]
 
 
 
Now consider application of a regression model to new data [step (4)
from above].  The betas have already been estimated.  If it is not con-
venient to measure the predictor variables from step (1), and one measures
surrogate variables perfectly, measurement error (rather than bias) has
occurred, even though the surrogate variables were measured perfectly.
In fact, when making a single prediction with a regression equation, if
the wrong values for the predictor variables are used, the predicted
value for the response variable would be incorrect due to measurement
error -- bias has no meaning in this context.
 
[When making a single prediction with a regression equation, bias
cannot occur in the *new* data (not the original sample data from which
the regression coefficients were estimated) -- only measurement error
could occur.  Is this interpretation reasonable?]
 
References for or against these interpretations would be most helpful.
Should I receive many e-mail response, I'll post a summary.  Thanks in
advance.
--
Mike Kattan  / Decision and Information Sciences  / University of Houston
Houston, TX 77204-6282 / 713-743-4734 / Fax x4693 / kattan@uhupvm1.uh.edu
==
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From: kyrre.svarva@avh.unit.no (Kyrre Svarva)
Subject: Re: Violated t-test assumptions: how to adjust?
Organization: University of Trondheim, Norway
Date: Tue, 26 Jan 93 20:28:05 GMT
X-News-Reader: VMS NEWS 1.11b

In <1993Jan25.195506.18216@motor.physiol.ox.ac.uk> ph@physiology.oxford.ac.uk writes:

> In his classic 1960 paper, Boneau discussed the effects on the t test
> and related tests of violating the parametric assumptions.  From the
> inferential point of view, I remember the most drastic problem is the
> situation of unequal sample sizes, where the smaller sample has the
> larger variance.

As far as I know there is a special version of the t-test that does not 
depend on homogeneity of variance. In this test, the SE estimate (the 
denominator in the t formula) is computed in a different manner so as to
adjust the value of t downwards in the situation you describe. The df's are 
also adjusted. This approach is described in Robert Johnson: Elementary
Statistics, chapter 10, and is available in SPSS ("Separate variance estimate
t-test") although there a different (better?) way of assessing the number
of df's is used. There also is something called the "Welch t-test",
which I believe is a different name for the same thing.

As to your particular situation: Although the t-test takes sample size into 
account, I would hesitate to draw conclusions with only 5 observations in one
sample.... with this sample size, one non-representative sample member could 
have a substantial effect on the mean as well as the variance of the sample.

- - What do you REAL statisticians out there think of these matters??

Kyrre Svarva
From: tsu1@vax.oxford.ac.uk
Newsgroups: bit.listserv.stat-l
Subject: TIME SERIES FORECASTING/KALMAN FILTER
Date: 26 Jan 93 20:44:03 GMT
Organization: Oxford University VAX 6620


I am working in a project dealing with the forecasting of traffic congestion
in urban road networks, for use in a real time drivers' information system.
I used formulations like

^y(t+1) = a1.y(t) + a2.y(t-1) + ..... +
          b11.q1(t) + b12.q1(t-1) + ..... +
          b21.q2(t) + b22.q2(t-1) + ..... +
            .......
          c1.yh(t+1) + c2.yh(t) + ...
          d11.qh1(t) + d12.qh1(t-1) + ..... +
          d21.qh2(t) + d22.qh2(t-1) + ..... +         (1)
         
          
where ^y(t+1) the predicted level of flow at time t+1 at the link of interest
       y(t)   the measured flow at the link of interest
       qi(t)  the measured flow at link i upstream of the link of interest 
       yh(t)  historical flow for the link of interest at time t
       qhi(t) historical flow for link i at time t
              (historical flow (t) = average flow(t) over the last few days) 

and kalman filter theory to identify the system parameters which are assumed to
be time varying.
I used several formulations (differnt terms of the AR and MA components)  but
I did not find any formulation that consistently performs better.

I then tried to predict the ratio between predicted and historical 
flow ^ry(t+1)=^y(t+1)/yh(t+1) and then from this ratio to predict the ^y(t+1)
using formulations like
  ^ry(t+1) = A1.ry(t) + A2.ry(t-1) + ...
             B11.rq1(t) + ........ 
             B21.rq2(t) + ....

   ^y(t+1) = ^ry(t+1).yh(t+1)

where   rqi(t) = qi(t)/qhi(t)

I found that the latter approach consistently performs better than the former.
There is often a regularity in the patterns observed every day, but given the
fact in both formulations I used historical info I didn't expect significant
differences in the forecasts.
Can anybody help me on how I can a develop a theoretical proof of this outcome?
Do you know of any papers discussing a relevant subject?


Thanks very much,

Petros Vythoulkas
TSU1@vax.oxford.ac.uk
Transport Studies Unit
University of Oxford
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Date:         Thu, 21 Jan 1993 15:52:46 PST
From:         Ping Ma <ping@STAT.UBC.CA>
Subject:      Re: r for nonlinear regression
              93 10:50 am

> the sd of the observation.  If unweighted, this is the same formula one
> might use for linear regression: sqrt(1-(sse/ssto)).  I don't see how it makes

Note that 1-sse/ssto = (ssto-sse)/ssto = propotion of total variation
in the response variable explained by the model (a nonlinear model
in this context). This proportion is also called the coeff of determination,
which measures how well a model fits the data.

> any sense for nonlinear regression.
>
> This can't make sense because it measures degree of fit TO A LINE, not to the
> nonlinear regression function.  (It seems to me.) The only r that would

The above is only a special case of linear regression --- one explantroy
variable. In this case, sqrt(1-(sse/ssto)) gives the correlation coeff
of the pair of variables (the response and explanatory) measuring the
strength of linear association of the pair of variables. Since the model
used is a linear model, the "fit" amounts "to a line". The fit could be
a hyper-plane (for linear mutliple regression) or a curve (for
non-regression), etc.

Hope this help.

-------------------------------------------------
Ping Hang Ma,
Statistics Department, UBC
Vancouver, B.C., Canada
E-Mail: ping@stat.ubc.ca
Tel:  (604) 822-5535
Fasx: (604) 822-6960
-------------------------------------------------
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Date:         Tue, 26 Jan 1993 16:48:29 MST
From:         Steve Ash <G9197769@NMSUVM1.BITNET>
Subject:      Interactions

In a recent discussion with a Professor of Marketing, a difference of
opinion arose regarding the interpretation of main effects when there
are interactions present.

To quote Geoffrey Keppel, "When an interaction is present, an investigator
will not be interested in the main effects anyway - anything that
might be said about the effects of one independent variable must be
qualified by a consideration of the levels of the other."
(Design and Analysis: A Researcher's Handbook, 2nd Ed, 1982, p.173)

How strictly is this to be held true? Should it depend upon the original
hypothesis and intention of the study? Any comments on the above quote?
Should interpretation always proceed sequentially from the highest level
interaction down to the main effects?

Thanks in advance,
Steve Ash
Dept. of Management
New Mexico State Univ.
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Date:         Thu, 21 Jan 1993 01:25:00 MST
From:         Haiyi Xie <SOCHX@FREUD.SBS.UTAH.EDU>
Subject:      How to skip variables in SAS

HI all,

For the following SAS file, is there any thort cut for me to skip some
variables (e.g. I just want to read pop71 pop75-pop77 and pop85).

++++++++++++++++++++++++++++++++++++

  data class;
   INFILE soc631.dat lrecl=320;
   INPUT  #1   CNAM1  $26.
          #2    (POP70-POP85)     (16*20.)
          #3    (GNP70-GNP85)     (16*20.)
          #4    (GDP70-GDP85)     (16*20.)
          #5    (EXP70-EXP85)     (16*20.)
          #6    (IMP70-IMP85)     (16*20.)
          #7    (GGC70-GGC85)     (16*20.)
          #8    (TTI70-TTI85)     (16*20.)
          #9    (LTI70-LTI85)     (16*20.)
          #10   (CAB70-CAB85)     (16*20.)
          #11   (FDI70-FDI85)     (16*20.)
          #12   (IMR70-IMR85)     (16*20.)
++++++++++++++++++++++++++++++++++++++++++++++++++++

Many thanks

H. Y. Xie
(801) 581-7922
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Date:         Fri, 29 Jan 1993 15:35:21 NZST
From:         David Baird <BAIRDD%LCNCRI-2@LAN.LINCOLN.CRI.NZ>
Subject:      Nei's Genetic Similarity Index

I have a client who has presence / absence data for DNA fragments
from 5 individuals for each of 10 different geographic
populations of a species of insect. The DNA fragments show as
bands on a gel. There are a variable number of bands per
population. The data looks like this:

                               Band
Population  Individual   1 2 3 4 5 6 7 8 9 ...
         1           1   0 1 1 0 1 0 0 0 0
                     2   1 1 0 1 1 0 0 0 0
                     3   1 1 0 0 1 0 0 0 0
                     4   1 1 1 1 1 0 0 0 0
                     5   0 0 1 0 1 0 0 0 0

         2           1   0 0 1 0 1 1 1 0 1
                     2   0 0 1 1 0 1 1 1 0
                     3   0 0 1 0 0 1 0 0 0
                     4   0 0 1 1 0 1 1 0 1
                     5   0 0 1 1 0 1 1 1 1
etc.
Let x    = 1 if the band k is present in population j, individual
     ijk     i, and 0 otherwise.

She wants to calculate a similarity matrix betweem populations
which reflects the number of shared bands between individuals.
She has a paper by Nei and Li, "Mathematical model for studying
genetic variation in terms of restriction endonucleases." Proc
National Acad Sciences USA (1979) which defines a general
similarity coefficient between populations in a theoretical
manner without giving any computational formulae.

Could someone who has used this approach, or any other define the
computational formulae in terms of my data, x    , above?
                                             ijk

Thanks for your help.

David Baird    Biometrician

Mail address:  AgResearch, PO Box 60, Lincoln, New Zealand
Internet:      BairdD@LAN.Lincoln.CRI.NZ
Phone:         +64 3 325 3011
Fax:           +64 3 325 2946
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Date:         Thu, 28 Jan 1993 12:39:00 EST
From:         "MARS::LEE_RB" <LEE_RB%MARS.decnet@APG-EMH7.ARMY.MIL>
Subject:      Groups of Binomial Data

      If I have a group of approximately 10 independent subjects each
following their own binomial distribution, what distribution should I expect
the group to follow?  How would the mean and variance for the group
be calculated?  Should I expect the group to follow a binomial distribution,
and if so how would I test to see if it did?
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Date:         Fri, 22 Jan 1993 11:08:18 +0100
From:         neurocomp@LUDENS.ELTE.HU
Subject:      ask for biomed books

Hallo. Could somebody recommend books on biometrics (neurometrics)
to my postgraduate course for physiologists, pharmacologists,
morphologists etc. published after 1987 ?
I need also some good reading statistical literature (primers)
(also 'young' ones).
Very many thanks in advance.

Nora Szilagyi
Eotvos Lorand Univ.
Dept. of Comp.Physiology
1088.Budapest,Muzeum krt.4/A
Hungary                                  e-mail: neurocomp@ludens.elte.hu
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Date:         Tue, 26 Jan 1993 13:47:45 HST
From:         Andy Taylor <ataylor@LALA.ZOO.HAWAII.EDU>
Subject:      testing for periodicity

I have several students who wish to determine whether there are
annual or lunar-monthly cycles in various biological variables.
I would appreciate any suggestions on how best to test for
periodicity, and in particular any recent references on this sort
of analysis. I've found references from the 70's and early 80's,
e.g. by Batschelet and Bliss, but wonder whether there is anything
newer.

Thanks for any and all help.

- Andy Taylor
  Department of Zoology            ataylor@lala.zoo.hawaii.edu
  University of Hawaii at Manoa    taylor@uhunix.bitnet
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Date:         Fri, 22 Jan 1993 01:57:31 -0500
From:         John Wendell <WENDELL@UHCCVX.UHCC.HAWAII.EDU>
Subject:      Libraian Position

Why Columbia University needs to post a librarian position on Stat-l I'm
not sure.  Maybe they are getting desperate after librarians nationwide
have been answering the call to boycott Columbia after the administration
decided to close the library school because it wasn't academic enough for
"Columbia" to train mere librarians.  I say let them try to run a
University without librarians, it serves them right!
- John WENDELL@UHCCVX.UHCC.HAWAII.EDU
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Date:         Fri, 22 Jan 1993 16:34:00 EST
From:         RTITZER@IUBACS.BITNET
Subject:      Cluster Analysis Problem

      We would like to use a clustering procedure to group similar
practice schedules, but we're not sure what to use as our proximity measure. In
the experiment subjects controlled the order in which they practiced three
tasks. Each subject performed 54 trials. Subjects were not restricted to
practice each task the same number of times. Here is part of an order that was
chosen by one subject (Trial Number, Task Number):

        1       2
        2       3
        3       3
        4       1
        ...    ...
       54       3

      Each subject chose a different order. We would like to form groups
of subjects based on the practice schedules they selected. We are most
interested in differentiating the subjects' practice schedules based on how
many times they practiced a task before they switched to a different task.
      We would appreciate suggestions on how to group these practice schedules.
                                                Bob & Jennifer
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