### Arc center

### PROC datasets to delete all temporary datasets

proc datasets library = work kill nolist;

quit;

### PROC GLIMMIX non-convergence problem solutions

Tips and Strategies for Mixed Modeling with SAS/STAT® Procedures Kathleen Kiernan, Jill Tao, and Phil Gibbs, SAS Institute Inc., Cary, NC, USA

ABSTRACT Inherently, mixed modeling with SAS/STAT® procedures, such as GLIMMIX, MIXED, and NLMIXED is computationally intensive. Therefore, considerable memory and CPU time can be required. The default algorithms in these procedures might fail to converge for some data sets and models. This paper provides recommendations for circumventing memory problems and reducing execution times for your mixed modeling analyses. This paper also shows how the new HPMIXED procedure can be beneficial for certain situations, as with large sparse mixed models. Lastly, the discussion focuses on the best way to interpret and address common notes, warnings, and error messages that can occur with the estimation of mixed models in SAS software.

http://support.sas.com/resources/papers/proceedings12/332-2012.pdf

glimmix data=xxx METHOD=RSPL ITDETAILS;

class xxx;

model xxx= xxx

/dist=binomial link=logit s ddfm=kr;

random int / subject = xxx;

NLOPTIONS MAXITER=100;

run;

### Tables available from PROC GLIMMIX

Output Added:

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

Name: ModelInfo

Label: Model Information

Template: Stat.Glimmix.ModelInfo

Path: Glimmix.ModelInfo

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

Output Added:

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

Name: ClassLevels

Label: Class Level Information

Template: Stat.Glimmix.ClassLevels

Path: Glimmix.ClassLevels

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

Output Added:

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

Name: NObs

Label: Number of Observations

Template: Stat.Glimmix.NObs

Path: Glimmix.NObs

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

Output Added:

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

Name: Dimensions

Label: Dimensions

Template: Stat.Glimmix.Dimensions

Path: Glimmix.Dimensions

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

Output Added:

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

Name: OptInfo

Label: Optimization Information

Template: Stat.Glimmix.OptInfo

Path: Glimmix.OptInfo

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

Output Added:

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

Name: IterHistory

Label: Iteration History

Template: Stat.Glimmix.IterHistory

Path: Glimmix.IterHistory

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

NOTE: Convergence criterion (GCONV=1E-8) satisfied.

NOTE: At least one element of the gradient is greater than 1e-3.

Output Added:

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

Name: ConvergenceStatus

Label: Convergence Status

Template: Stat.Glimmix.ConvergenceStatus

Path: Glimmix.ConvergenceStatus

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

Output Added:

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

Name: FitStatistics

Label: Fit Statistics

Template: Stat.Glimmix.FitStatistics

Path: Glimmix.FitStatistics

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

Output Added:

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

Name: CovParms

Label: Covariance Parameter Estimates

Template: Stat.Glimmix.CovParms

Path: Glimmix.CovParms

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

Output Added:

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

Name: ParameterEstimates

Label: Solutions for Fixed Effects

Template: Stat.Glimmix.ParameterEstimates

Path: Glimmix.ParameterEstimates

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

Output Added:

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

Name: Tests3

Label: Type III Tests of Fixed Effects

Template: Stat.Glimmix.Tests3

Path: Glimmix.Tests3

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

### Bonferroni correction for power analysis with more than two groups

I'm trying to find a textbook reference for the following procedure written explicitly in the context of power analysis. Please let me know if you know (k u e k a w a AT gmail com).

When there are more than two conditions in the experiment design, the alpha level, one of the parameters that go into power analysis, can be divided by the number of contrasts. If there are three groups (control, treatment 1, treatment 2), there are three contrast points:

C vs T1, C vs T2, and T1 vs T2.

The typical alpha level is 0.5, so you can do:

0.5 / 3 = 0.16

and use that in the power analysis software.

If only two contrasts are important for your purpose:

0.5 / 2 = 0.25

Reference:

page 24 of

https://medschool.vanderbilt.edu/cqs/files/cqs/media/2010Ayumi.pdf

Bonferroni correction:

### SAS PROC GLIMMIX method=

The default technique is METHOD=RSPL, corresponding to maximizing the residual log pseudo-likelihood with an expansion about the current solutions of the best linear unbiased predictors of the random effects. In models for normal data with identity link, METHOD=RSPL and METHOD=RMPL are equivalent to restricted maximum likelihood estimation, and METHOD=MSPL and METHOD=MMPL are equivalent to maximum likelihood estimation.

***

The following SAS Usage Note:

http://support.sas.com/kb/37107

http://support.sas.com/kb/40724

provide information on testing covariance parameters when using PROC MIXED and PROC GLIMMIX.

### Using PROC LOGISTIC to Estimate the Rasch Model

Paper 342-2011

Using PROC LOGISTIC to Estimate the Rasch Model

Tianshu Pan, Pearson Yumin Chen, the University of Texas Health Science Center at San Antonio ABSTRACT

This paper describes how to use PROC LOGISTIC to estimate the Rasch model and make its estimates consistent with the results of the standard Rasch model software WINSTEPS.

http://support.sas.com/resources/papers/proceedings11/342-2011.pdf

### SAS Proc POWER examples

Comparison of two independent groups:

proc power;

twosamplemeans test=diff

groupmeans = 0 | .2

stddev = 1

npergroup = .

power = .8;

run;

Comparison of dependent data (paired)

proc power;

pairedmeans test=diff

meandiff = .2

corr = 0.5

stddev = 1

npairs = .

power = .8;

run;

Comparison of proportions

proc power;

twosamplefreq test=pchi

groupproportions = (.65 .70)

nullproportiondiff = 0

power = .80

npergroup =.;

run;

### Logit coefficients from logistic regression model

How do we interpret logic coefficients estimated by logistic regression model? The following is a hypothetical result:

log(p/1-p) = 0.3 + 0.2*Male + 0.4*TREATMENT

One use of this result is to see if Male effect and GPA effect are statistically significant. We also want to know the meaning of values, such as 0.2 and 0.4. Because the left side of equation is a complex mathematical construct, it is not immediately clear what 0.2 or 0.4 means.

<Under construction>