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NEW IN SAS 9.2

STATISTICAL PROCEDURES

SUN LI

SENIOR STATISTICIANCENTRE FOR ACADEMIC COMPUTING

LSUN@SMU.EDU.SG

mailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sgmailto:lsun@smu.edu.sg

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OUTLINE

Useful links

Base SAS

SAS/ACCESS

SAS/STAT

SAS Stat Studio software

SAS Power and Sample Size (PSS application)

PROC GENMOD Bayesian capability

PROC GLIMMIX Generalized linear mixed model

SAS/ETS : PROC COUNTREG - ZINB model

SAS/OR: SAS Simulation Studio2

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USEFUL LINKS

SAS 9.2 Documentation:

Whats new in SAS 9.2?

http://support.sas.com/documentation/cdl/en/whatsnew/61982/HTML/default/acpcrefwhatsnew902.htm

http://support.sas.com/documentation/cdl/en/whatsnew/62435/PDF/default/whatsnew.pdf

Training notes & example data sets:http://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS-Training%20Slides%20and%20Syntax.aspx

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http://support.sas.com/documentation/cdl/en/whatsnew/61982/HTML/default/acpcrefwhatsnew902.htmhttp://support.sas.com/documentation/cdl/en/whatsnew/61982/HTML/default/acpcrefwhatsnew902.htmhttp://support.sas.com/documentation/cdl/en/whatsnew/62435/PDF/default/whatsnew.pdfhttp://support.sas.com/documentation/cdl/en/whatsnew/62435/PDF/default/whatsnew.pdfhttp://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS-Training%20Slides%20and%20Syntax.aspxhttp://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS-Training%20Slides%20and%20Syntax.aspxhttp://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS-Training%20Slides%20and%20Syntax.aspxhttp://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS-Training%20Slides%20and%20Syntax.aspxhttp://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS-Training%20Slides%20and%20Syntax.aspxhttp://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS-Training%20Slides%20and%20Syntax.aspxhttp://support.sas.com/documentation/cdl/en/whatsnew/62435/PDF/default/whatsnew.pdfhttp://support.sas.com/documentation/cdl/en/whatsnew/62435/PDF/default/whatsnew.pdfhttp://support.sas.com/documentation/cdl/en/whatsnew/61982/HTML/default/acpcrefwhatsnew902.htmhttp://support.sas.com/documentation/cdl/en/whatsnew/61982/HTML/default/acpcrefwhatsnew902.htm

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Enhanced procedures to discuss:

PROC MEANS

PROC UNIVARIATE

PROC FREQ

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BASE SAS

1. PROC MEANS

The PRT statistic is now an alias for the PROBT statistic

The MODE statistic can now be used with PROC MEANS.

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BASE SAS

2. PROC UNIVARIATE

CDFPLOT: plots the observed cdf of a variable and enables you tosuperimpose a fitted theoretical distribution on the graph.

PPPLOT: creates a P-P plot which compares the ecdf of a variable

with a specified theoretical cumulative distribution function.

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BASE SAS

3. PROC FREQ

Testing for specified proportions

Distribution plot and other plots

Binomial proportion tests and confidence intervals

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Support new file formats:

dBase with memos (DBFMEMO), JMP, Paradox DB, SPSS

SAV, Stata DTA

Date/Time Value:

USEDATE=YES /NO option in PROC IMPORT

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New related software:

SAS Stat Studio software

SAS Power and Sample Size (PSS application)

New procedures:

Bayesian capabilities are introduced under procedures:GENMOD, LIFEREG, and PHREG

GLIMMIX, GLMSELECT, and QUANTREG

Experimental procedures: HPMIXED, MCMC, SEQDESIGN, SEQTEST ,TCALIS

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SAS/STAT

1. SAS Stat Studio 3.1

New software for data exploration and analysis

Start > All Programs > Stat Studio 3.1

Demo basic steps with hands-on: Graph & Polynomial regression with data set:

hurricanes.sas7bdat

Transform data & Model fitting with data setbaseball.sas7bdat

SAS help and documentation:

SAS products -> SAS Stat Studio 3.1 -> SAS Stat Studio 3.1Users Guide

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SAS/STAT

2. SAS Power and Sample Size 3.1

PSS application

Start > All Programs > SAS > SAS Power and Sample Size> SAS Power and Sample Size 3.1

Demo basic steps with hands-on:

Power analysis for One Sample T Test

SAS help and documentation:

SAS products -> SAS/STAT -> SAS/STAT Users Guide ->The Power and Sample Size Application

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SAS/STAT

3. PROC GENMOD Bayesian Analysis of a Linear Regression Model:

Here is a study of 54 patients undergoing a certain kind of liveroperation in a surgical unit. The data setSurg contains survival

time and certain covariates for each patient.

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SAS/STAT

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SAS/STAT

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BAYES : produces Bayesian analysis via Gibbs sampling for most of thestatistical analyses.

SEED: specifies randomization seed. It is used to maintain reproducibility.

OUTPUT : saves the samples in the SAS data set PostSurg for furtherprocessing.

By default, a uniform prior distribution is assumed on the regressioncoefficients. A noninformative gamma prior is used for the scale parametersigma.

ODS Graphics is enabled as specified in the SAS statements to display thediagnostic plots.

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SAS/STAT

There is a 1.00 probability of a positive

relationship between the logarithm of a

blood clotting score and survival time,adjusted for the other covariates.

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SAS/STAT

4. PROC GLIMMIX

Comparison btw GLIMMIX and other procedures

1) Response can have a nonnormal distribution (MIXED assumesnormally distributed response.)

2) Incorporates random effects in the model and so allows forconditional and marginal inference (GENMOD allows only formarginal inference.)

3) The class of generalized linear mixed models is a special case of the

nonlinear mixed models; hence some of the models you can fit withNLMIXED can also be fit with GLIMMIX.

(The details can be found in the SAS help documentation.)

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Logistic Regressions with Random Intercepts

Researchers investigated the performance of banks in a multicenterstudy. They randomly selected 15 centers. One of the study goals wasto compare the occurrence of loan defaults for the banks. In eachcenter two types of banks were selected and coded as A" and "B.Under each type, there are a few of banks selected for the study.

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SAS/STAT

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To model the probability of defaults happened in the two types of banks:A andB, you need to account for the fixed group effect and the randomselection of centers. We assume a linear model that relates group andcenter effects to the logit of the probabilities:

A-B measures the difference in the logits.

0 is the overall intercept in the model. 21

SAS/STAT

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SAS/STAT

CLASS : specifies the classification variables.

MODEL : specifies response variable as a sample proportion by using theevents/n syntax.

SOLUTION : requests a list of solutions for fix-effects parameter estimates.Because the default/n syntax, the procedure gives binomial distribution withlogit link.

RANDOM : specifies that the linear predictor contains an intercept term that

randomly varies at the level ofcentereffect. In other words, a randomintercept is drawn separately and independently for each center in the study.

LSMEANS : requests the least squares means of the group effect on the logitscale.

ILINK: adds estimates, standard errors, and confidence limits on the mean(probability) scale .

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SAS/STAT

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SAS/STAT

The details can be found in the SAS help documentation in the

individual procedures.

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SAS/ETS

ZIP and ZINB Models for Data Exhibiting Extra Zeros

An often encountered characteristic of count data is that the number ofzeros in the sample exceeds the number of zeros predicted by either thePoisson or negative binomial model.

Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB)models explicitly model the production of zero counts to account forexcess zeros.

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SAS/ETS

This study examines how factors such as gender (fem), marital status (mar),number of young children (kid5), prestige of the graduate program (phd),and number of articles published by a scientists mentor (ment), affect thenumber of articles (art) published by the scientist.

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SAS/ETS

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SAS/ETS

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SAS Simulation Studio

Other enhanced features, see theSAS/OR documentation.

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