I am working on a linear mixed model to compare results in SAS proc glimmix (or equivalently proc mixed) and CRAN R lme4 package. My challenge is: How do I manipulate the SAS code to get similar (approximately equal) F-ratios for the analysis of variance in SAS and R?
I tried to control for convergence in SAS (see code), but I need help to finetune that SAS code to get the degrees of freedom and/or F-ratio that match those i get in R.
Data extract (1st 5 observations):
Here is my SAS code:
proc glimmix data=BarleyGroups outdesign=XZ ; class factorA factorB groupA groupB switchA switchB ; model yield= groupA|groupB / ddfm=kr; random groupA*factorA*switchA; random groupA*groupB*factorB*switchB; random groupB*factorB*switchB; random groupB*groupA*factorA*switchA; random groupA*factorA*switchA*groupB*factorB*switchB; parms (0.0000) (0.0000) (0.0000) (0.0000) (0.07074) (0.41458); nloptions maxiter=1000 maxfunc=10000 xconv=1e-5 gconv=1e-5 technique=nmsimp; lsmeans groupA|groupB / lines ; Title "Linear Mixed Model with Convergence"; run;
Here is my SAS output from the code above:
**********************************************************************************************************************
Here is my R code:
# Load necessary libraries library(lme4) library(emmeans) library(lmerTest) # Provides Type III tests for fixed effects
control_settings <- lmerControl( optimizer = "Nelder_Mead", # Use Nelder-Mead optimizer optCtrl = list(maxfun = 10000), # Set maximum number of function evaluations check.conv.grad = .makeCC(action = "warning", tol = 1e-5), # Gradient convergence check check.conv.singular = .makeCC(action = "warning", tol = 1e-5) # Singular fit check ) Full.mod<-lmer(Yield~groupA+ groupB+groupA:groupB+ (1 | groupA:factorA:switchA) + (1 | groupB:factorB:switchB) + (1 | groupA:groupB:factorB:switchB) + (1 | groupB:groupA:factorA:switchA)+ (1|groupA:factorA:switchA:groupB:factorB:switchB), data = BarleyGroups, control = control_settings) summary(Full.mod) anova(Full.mod, type=3, ddf = "Kenward-Roger")
Here is the R output:
> anova(Full.mod, type=3, ddf = "Kenward-Roger")
Type III Analysis of Variance Table with Kenward-Roger's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
groupA 0.08694 0.04347 2 4.5808 0.1040 0.9033
groupB 1.51768 0.50589 3 5.5396 1.2056 0.3905
groupA:groupB 1.38705 0.23118 6 7.9107 0.5506 0.7585
**************************************************************************************************************************************
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