proc datasets library = work kill nolist;
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.
glimmix data=xxx METHOD=RSPL ITDETAILS;
model xxx= xxx
/dist=binomial link=logit s ddfm=kr;
random int / subject = xxx;
Label: Model Information
Label: Class Level Information
Label: Number of Observations
Label: Optimization Information
Label: Iteration History
NOTE: Convergence criterion (GCONV=1E-8) satisfied.
NOTE: At least one element of the gradient is greater than 1e-3.
Label: Convergence Status
Label: Fit Statistics
Label: Covariance Parameter Estimates
Label: Solutions for Fixed Effects
Label: Type III Tests of Fixed Effects
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
page 24 of
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:
provide information on testing covariance parameters when using PROC MIXED and PROC GLIMMIX.
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.
Comparison of two independent groups:
groupmeans = 0 | .2
stddev = 1
npergroup = .
power = .8;
Comparison of dependent data (paired)
meandiff = .2
corr = 0.5
stddev = 1
npairs = .
power = .8;
Comparison of proportions
groupproportions = (.65 .70)
nullproportiondiff = 0
power = .80
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.