HDTenEightyP/NetNews huggingface dataset:
X-Envelope-to: stat-l@MCGILL1.BITNET
Newsgroups: bit.listserv.stat-l
Date:         Thu, 15 Oct 1992 18:21:03 EDT
From:         leanne <lawhiteside@UALR.EDU>
Subject:      lisrel/prelis with SPSS ... help!

I am trying to test whether the correlation matrices of observed variables
are the same for different groups.  I have 8 sites (var SITE) and each site
has treatment/control (var TG) so I want to test the equality of the 16
matrices (composed of 16 vars).  The program I put together is below ...
using the Prelis and Lisrel manuals.  I should also say that I am
running version 7 of LISREL on a DEC/VAX VMS system.

When I execute the program it seems to get the raw data ok, and does at
least one execution of Prelis but then I get a strange message:

PRELIS returning to VMS.
%DCL-W-IVQUAL, unrecognized qualifier - check validity, spelling, and placement
$ SET NOVERIFY
$Input file            [SYS$INPUT]:
PRL_CMD_348000C2_65D559B9.TMP
$Output file          [SYS$OUTPUT]:
SYS$OUTPUT



then it seems to try to continue with PRELIS again... and it continues
indicating different matrices (and sample sizes)  until it ends with the
usual SPSS ending with no errors - but I think there are warnings ...
I would include the LIS file but it did strange things when I tried to
edit it ....

Any suggestions would be appreciated....





****** LISREL program follows *****

set mxwarns=20000
set undefined=nowarn
data list file = 'raw.dat'
   free/ SITE (A3) TG (A)
     HTO_12   HRE_12 HAC_12 HOR_12
     HPM_12 HIN_12 HVA_12 HTO_36  HAS_36
     HLA_36 HAC_36 HLE_36 HMO_36 HPE_36 HVA_36 HWA_36
SORT CASES BY SITE TG
SPLIT FILES BY SITE TG
PRELIS
  /VARIABLES =
     HTO_12   HRE_12 HAC_12 HOR_12
     HPM_12 HIN_12 HVA_12 HTO_36  HAS_36
     HLA_36 HAC_36 HLE_36 HMO_36 HPE_36 HVA_36 HWA_36
 /TYPE=COVARIANCE
SPLIT FILE OFF
LISREL
  /DA NI=16 NO = 67 NG = 16 MA = KM
  /MO NX = 16 NK = 16 PHI = ST LX = DI,FR TD = ZE
  /OU MI RS
  /DA NI=16 NO =  41 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 71 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 42 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 79 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 41 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 49 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 40 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 49 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 43 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 64 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 39 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 63 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 40 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 54 NG = 16 MA = KM
  /MO PH = IN
  /OU MI RS
  /DA NI=16 NO = 37 NG = 16 MA = KM
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Acknowledge-To: <ELLEN@PACEVM>
Newsgroups: bit.listserv.stat-l
Date:         Fri, 9 Oct 1992 09:09:08 EDT
From:         arthur s ellen <ELLEN@PACEVM.BITNET>
Subject:      Disciminant analysis with dichotomies?

I have a student who has two clinical groups and one non-referred group.
In her study she is interested in determining how a number (about 14)
variables predict group membership in order to infer something about
the effectiveness of using these variables in clinical identification.
Two of the variables are continuous the rest are dichotomies. I would
have liked to perform a discriminant analysis but I believe the mix
of continous and dichotomous variables prevents this. Does any one
have an alternative suggestions for a similar kind of analysis?

------------------------------------------------------
      ART ELLEN             PSYCHOLOGY DEPARTMENT  
 BITNET:  ELLEN@PACEVM         PACE UNVERSITY      
  VOICE:  (212) 346-1506         41 PARK ROW       
                           NEW YORK, NY 10038-1502 
------------------------------------------------------
Organization: Arizona State University
Date: Thursday, 8 Oct 1992 17:12:36 MST
From: <ICWAS@ASUACAD.BITNET>
Newsgroups: bit.listserv.stat-l
Subject: Re: Designed Experiments Texts

You might want to look at:

          Understanding Industrial Designed Experiments (3rd Edition)
          (Blending the Best of Classical, Shainin & Taguchi Approaches)

          by Stephen R. Schmidt and Robert F. Launsby

          Air Academy Press, Colorado Springs, CO

          Contact Schmidt/Launsby Consulting
                  PO Box 62099
                  Colorado Springs, CO  80962

                  (719) 531-0777
                  (719) 531-0778 FAX

This book presents a good, industrial approach to DOE and has a great set
of case studies.  The copy floating around our department also came with
a student version of Q-Edge Software and a factory simulation program.
From: dkw4@NIOSHE2.EM.CDC.GOV
Newsgroups: bit.listserv.stat-l
Subject: Akaike's Info. Crit. & PROC MIXED
Date: 15 Oct 92 21:30:00 GMT
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SAS Institute's new manual for Version 6.07 lists some possible information
(such as AIC - Akaike's Information Criterion) which the user can obtain with
one of its new procedures, PROC MIXED.  In their example, they show a
negative value for the AIC, along with a comment that "larger values are
preferred".

I thought AIC was equal to -2*ln(likelihood) + 2*(number of
parameters) - or something akin to that ...therefore, it would seem
impossible that this could ever be negative.  Further, it would seem like
smaller values are better.

Does this make sense to anyone?

Thanks much.

Deanna

*-----------------------------------------------------------------*
Deanna Wild
Statistician
National Institute for Occupational Safety and Health
4676 Columbia Parkway  MS: R-4
Cincinnati, Ohio  45226

INTERNET: DKW4@NIOSHE2.EM.CDC.GOV
*-----------------------------------------------------------------*
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 9 Oct 1992 14:56:00 MST
From:         DAVE HUTH <DHUTH@UTAHCCA.BITNET>
Subject:      Re: Independence of observations in logistic regression

i believe the original problem was that subjects were contributing more
than one observation to the data.  this *is* a problem that should be
dealt with.

an old approach to handle time-varying covariates in event history
analysis was to generate multiple observations for each subject
(person year file), and use logistic regression to estimate
parameters.  Paul Allison has a paper in Sociological Methodology,
1982, pp 61-98, "Discrete-time methods for the analysis of event
histories", in which there is a discussion on the dependence of
observations.

dave huth
Encoding: 6 TEXT
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Newsgroups: bit.listserv.stat-l
Date:         Sat, 10 Oct 1992 10:16:00 EST
From:         mmz1@CCDDCD1.EM.CDC.GOV
Subject:      F tests when scores are responses

Ordinal logistic regression with programs like PROC LOGISTIC (from SAS) is a
possible approach to your problem.  Cf., Alan Agresti's book on ordinal
categorical data analysis for other variants.

                            Matthew Zack
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
From: rolf@MATH.UNB.CA ((Rolf Turner))
Newsgroups: bit.listserv.stat-l
Subject: Re: Data snooping.
Date: 14 Oct 92 21:33:21 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

I have the impression that quite a bit of the recent discussion on
data snooping has been off-base and mis-represents what the phrase
``data snooping'' actually means.

Exploratory data analysis --- looking for patterns and making
discoveries --- is not data snooping.  Data snooping consists in
formulating and formally testing hypotheses based upon one's
exploration of the data rather than upon a priori conjectures.  As
such it renders probability statements associated with the results of
such hypothesis tests essentially meaningless.

Someone in the discussion has suggested reporting the p-values of
such post facto hypothesis tests as a form of descriptive
statistics.  There is clearly no intrinsic harm in so doing, as long
as these p-values are clearly distinguished as being ``descriptive''
rather than having a genuinely probabilistic meaning.  However, the
general devotion to p-values and 0.05 significance levels being what
it is, it seems to me that such a practice runs a serious risk of
being misinterpreted.  (The warning that these p-values are not
***really*** p-values will be treated as ``fine print'' and ignored.)


					cheers,

						Rolf

Rolf Turner
Dep't. of Maths and Stats
U.N.B., Fredericton, N.B. Canada E3B 5A3
rolf@math.unb.ca

----------------------------------------------------------------------
|                                                                    |
|          Nice day, isn't it?                                       |
|                                                                    |
----------------------------------------------------------------------
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Date:         Tue, 13 Oct 1992 09:03:10 EDT
From:         "(Robert I. Kabacoff Ph.D.)" <kabacoff@NOVAVAX.NOVA.EDU>
Subject:      path question revised

In my last request I mistated my question. I meant to say that by
adding variables to a path model an investigator here found the fit
improving (chi-square decreasing) but the percent variance accounted
for decreasing. I did not think this was possible. Here is the question
revised:


Please forgive me if this question is basic. I faculty member came
to me with the following situation.

She is doing a path analysis (recursive and all variables observed) and
has found that adding variables to the model improved the goodness of
fit (chi square) while decreasing the amount of variance accounted for
in the criterion variable.

I did not think that this was possible. Am I missing something?
--

=============================================================================
Rob Kabacoff, Ph.D.              | Internet: kabacoff@novavax.nova.edu
Center for Psychological Studies | Phone: (305) 475-7581
Nova University, College Ave.    | "Florida is Florida...it's warm, there are
Ft. Lauderdale, FL 33314         |  oranges, and safe driving is optional."
=============================================================================
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 16 Oct 1992 09:01:38 ADT
From:         "(Rolf Turner)" <rolf@MATH.UNB.CA>
Subject:      Weird subject line.

Reading my email this morning I found a heap of STAT-L items with the
subject line ``RETURNED BY ADMINISTRATOR''.  These items included some
postings which I had previously read on the list, some which I hadn't,
and a posting of my own.  Has this happened to anyone else?  Can
someone tell me what's going on?


Rolf Turner
Dep't. of Maths and Stats
U.N.B., Fredericton, N.B. Canada E3B 5A3
rolf@math.unb.ca

----------------------------------------------------------------------
|                                                                    |
|          Nice day, isn't it?                                       |
|                                                                    |
----------------------------------------------------------------------
From: henrik@stat.tamu.edu (Henrik Schmiediche)
Newsgroups: bit.listserv.stat-l
Subject: Multivariate Time Series Analysis Software
Date: 15 Oct 92 15:38:47 GMT
Organization: Department of Statistics, TAMU


      Hi,

I am trying to survey the availability of software to perform
multivariate time series analysis. By multivariate I mean 3 or more
data sets. If anyone knows of software packages, public domain, free or
comercial, could you send me a quick email with the name of the
software package? If you happen to know the scope of the analysis that
the package performs (ie, spectral estimation, AR spectral estimation,
window estimators, etc.) I would appreciate that info too.

Any response will be appreciated. If you know of a bibliography and/or
references concerning this matter, that info would be helpful too.

Thanks,

      - Henrik



--
Henrik Schmiediche, Dept. of Statistics, Texas A&M, College Station, TX 77843
E-mail: henrik@stat.tamu.edu  |  Tel: (409) 845-9447   |  Fax: (409) 845-3144
From: dkw4@NIOSHE2.EM.CDC.GOV
Newsgroups: bit.listserv.stat-l
Subject: AIC
Date: 16 Oct 92 20:12:00 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
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Encoding: 59 TEXT
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Dear STAT-Lers,

A SAS Institute person saw my earlier question about the puzzling negative
sign on the AIC statistic in PROC MIXED.  What follows is his helpful
response....

Deanna
*-----------------------------------------------------------------*
Deanna Wild
Statistician
National Institute for Occupational Safety and Health
4676 Columbia Parkway  MS: R-4
Cincinnati, Ohio  45226

INTERNET: DKW4@NIOSHE2.EM.CDC.GOV
*-----------------------------------------------------------------*



------------------------------------------------------------------------------
FORWARDED FROM: Wild, Deanna K.
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Date: Fri, 16 Oct 1992 08:50:35 -0400
From: Russell Wolfinger <sasrdw@unx.sas.com>
Message-Id: <199210161250.AA13144@bayes.unx.sas.com>
To: DKW4@NIOSHE2.EM.CDC.GOV
Subject: AIC
Deanna,

I caught your message on the net, and wanted to let you know that
there are 2 different forms for aic.  The first, used by PROC MIXED,
is larger-the-better and is given on p. 326 of SAS Tech Report P-229.
The second is smaller-the-better, and is -2 times the first.  Both
can be either positive or negative, because the likelihood is computed
from ordinates of the normal probability distribution, which can
be greater than 1.  Because of comments like yours, we may switch the
form of aic to the second one in a future release.  Thanks for your
interest!

--------------------------------------------------------------------
Russ Wolfinger           `Which way ought I to go to get from here?'
SAS Institute Inc. J-4   `That depends a good deal on where you
Cary, NC 27513-2414          want to to get to,' said the Cat.
(919) 677-8000 x6695     `I don't much care where--' said Alice.
sasrdw@unx.sas.com       `Then it doesn't matter which way you go,'
                             said the Cat.

                                  Lewis Carroll, Alice in Wonderland
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:      UK.AC.HULL.SEQ; 13 OCT 92 10:50:17 BST
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Original-Sender: R.A.Reese@uk.ac.hull.seq
Newsgroups: bit.listserv.stat-l
Date:         Tue, 13 Oct 1992 06:01:39 -0400
From:         Allan Reese <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Implausible values.

We run a laser printer in an internal unventilated room. It requires
maintenance. The engineer suggested it was being abused by the
environment and was overheating (people have complained about this
room for years but who cares about them - rhetorical). I installed
a standard max-min and put a form for whoever was there to record the
temperature at set times over a few days, plus a 24-hour maximum.

Results: the temperature in the room is almost constant. Readings over 2
weeks vary from 76F to 81F, across a day or over a weekend. Wedging the
door open during the day resulted in temperatures lower by 1 or 2
degrees F.

What the hell has this to do with stat-l?: the instrument is graduated
in degrees F and C. One entry reads 60/15. From looking at the instrument
I guess that the observer read the TOP of the needle in the MIN branch
of the thermometer. Another entry reads 78/35.

If I were asked to report these data, I would give the results as stated
in para 2. If I were being punctilious, I would report that two
observations had been discarded as unbelievable. The upshot is that
any statistical test on these data based on them being a "random
sample" is inherently flawed, but what do you REALLY want - mathematical
nonsense or believable results?

--
(R.) Allan Reese        Janet:          r.a.reese@uk.ac.hull
Head of Applications    Direct voice:   +44 482 465296
Computer Centre         Voice messages: +44 482 465685
Hull University         Fax:            +44 482 466441
Hull HU6 7RX, U.K.
From: NARAYANA@UCS.INDIANA.EDU (A. NARAYANAN DECISION & INFO. SYSTEMS--855-2665)
Newsgroups: bit.listserv.stat-l
Subject: discrim. analysis with dichotomies
Date: 14 Oct 92 17:22:51 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

>I have a student who has two clinical groups and one non-referred group.
>In her study she is interested in determining how a number (about 14)
>variables predict group membership in order to infer something about
>the effectiveness of using these variables in clinical identification.
>Two of the variables are continuous the rest are dichotomies. I would
>have liked to perform a discriminant analysis but I believe the mix
>of continous and dichotomous variables prevents this. Does any one
>have an alternative suggestions for a similar kind of analysis?
>


W.J. Krzanowski has done some work on discriminant analysis with mixtures
of continuous and categorical variables. He has defined a new measure of
distance for this case using distance measures originally proposed by
Matusita and has looked at discriminant analysis from a descriptive as
well as hypothesis testing prespectives. The following references should
help:

Krzanowski, W.J. (1980). Mixtures of continuous and categorical variables
in discriminant analysis, Biometrics, 36, 493-9.

Krzanowski, W.J. (1982). Mixtures of continuous and categorical variables
in discriminant analysis:a hypothesis testing approach, Biometrics, 38,
991-1002.

Krzanowski, W.J. (1983). Distance between populations using mixed
continuous and categorical variables, Biometrika, 70, 235-43.

A. Narayanan,
School of Business
Indiana University
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Newsgroups: bit.listserv.stat-l
Date:         Thu, 15 Oct 1992 17:07:00 EST
From:         POSTOFFICE@ROUTER.EM.CDC.GOV
Subject:      RETURNED BY ADMINISTRATOR

>I have a student who has two clinical groups and one non-referred group.
>In her study she is interested in determining how a number (about 14)
>variables predict group membership in order to infer something about
>the effectiveness of using these variables in clinical identification.
>Two of the variables are continuous the rest are dichotomies. I would
>have liked to perform a discriminant analysis but I believe the mix
>of continous and dichotomous variables prevents this. Does any one
>have an alternative suggestions for a similar kind of analysis?
>


W.J. Krzanowski has done some work on discriminant analysis with mixtures
of continuous and categorical variables. He has defined a new measure of
distance for this case using distance measures originally proposed by
Matusita and has looked at discriminant analysis from a descriptive as
well as hypothesis testing prespectives. The following references should
help:

Krzanowski, W.J. (1980). Mixtures of continuous and categorical variables
in discriminant analysis, Biometrics, 36, 493-9.

Krzanowski, W.J. (1982). Mixtures of continuous and categorical variables
in discriminant analysis:a hypothesis testing approach, Biometrics, 38,
991-1002.

Krzanowski, W.J. (1983). Distance between populations using mixed
continuous and categorical variables, Biometrika, 70, 235-43.

A. Narayanan,
School of Business
Indiana University
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Vmsmail-To: SMTP%"stat-l@vm1.mcgill.ca"
Newsgroups: bit.listserv.stat-l
Date:         Mon, 12 Oct 1992 09:51:38 -0500
From:         MGRANAAS@CHARLIE.USD.EDU
Subject:      responses to: another logistic reg question

Hi all,

Many people responded to my request for references on polytomous logistic
regression.  By far the most complete list was provided by Cande Ananth
at UNC-Chapel Hill, and was posted to the list, so I won't repeat it here.
A couple of other sources include:

	Alderich and Nelson (1984). Linear probability, logit and probit
		models.  Sage.

	Agresti (1990) Categorical data analysis.

The Hosmer and Lemeshow, and Agresti texts were mentioned by the largest
number of respondents.  I'm not sure what that means, but it seems that it
should be important.

Thanks to all who responded.

Mike Granaas
mgranaas@charlie.usd.edu
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 16 Oct 1992 09:08:27 EDT
From:         Toti Perez <TOTI@NERVM.BITNET>
Subject:      Power analysis

I'm needing a refresher on doing a power analysis for my dissertation.  My
study includes 5 dependent variables.  I plan to perform a canonical analysis,
multiple regression, and Hotteling's trace (a within-groups design).

I'd appreciate any help in jarring my memory again as well as any helpful
texts/resources.

Thanks!

Toti Perez
University of Florida
TOTI@NERVM
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 12 Oct 1992 12:42:17 CST
From:         April Milliken <BIOM030@UNLVM.BITNET>
Subject:      Need help with PCA

I originally sent this message to EDSTAT...but I would like to hear more
responses.  I guess I need both statistical help and educational help.  Please
respond directly to me.  Thank you.

Original message:

I need some help with a consulting problem...I lack in experience (I am a
grad student) and so I would like to hear from "experts".

I am working with a professor in animal science kindly named "Mr. Means".
He wants to publish correct results, but he seems to have difficulties in
understanding anything beyond one-way anovas.  Consequently, I have two
problems:

1)  The data are biochemical results; the machine (I am told) sometimes
    fails to give a correct result due to lack of solution (or whatever
    excuse).  Consequently, Mr. Means went through the data and elminated
    results that he did not feel were plausible (say a pH value that was
    higher than the rest of the values).  If they are removed, should I
    remove that observation on that animal, or remove the entire animal
    (keep in mind that only 15 animals are used in the study)?

2)  This is a perfect example of needing a principal components analysis
    in one area and repeated measures in another.  HOWEVER, this is WAY
    beyond the comprehensive power of Mr. Means.  I ran a PCA and came up
    with some insightful results...yet, I am at a loss as where to go
    from here; do I push PCA with 8 variables, or do 8 different one-way
    ANOVAs for Mr. Means?

I hope someone can help me out here.  I am at a loss....I NEED HELP!

Thanks in advance.

April Milliken
Department of Biometry
University of Nebraska-Lincoln
BIOM030@unlvm1.unl.edu

"I may be in Nebraska, but my heart belongs to Kansas State University!"