Dear R mixed model group,

I am testing a couple of logistic regression longitudinal models using lmer.  I got stuck in a position where I found that the correlation between random effects is 1.00 (intercept and slope -model with one term for the same grouping factor), while the std. deviation is very low for the slope.  Then, I tested another model with more than one term for the same grouping factor.  The LRT test p value is not significant.  Is it okay to keep the second model in this case?

Thanks.

AK
Random effects:
 Groups  Name        Variance   Std.Dev. Corr  
 resid   (Intercept) 10.9575146 3.310214       
 Subject (Intercept)  0.8220584 0.906674       
         time         0.0041092 0.064103 1.000 
Number of obs: 392, groups: resid, 392; Subject, 25


> anova(fm2_BDat,fm1_BDat)
Data: Behavdat
Models:
fm2_BDat: Response3 ~ 1 + Wavelength * Start_Resp * time + (1 | resid) + 
fm2_BDat:     (1 | Subject_BDat) + (0 + time | Subject_BDat)
fm1_BDat: Response3 ~ 1 + Wavelength * Start_Resp * time + (1 | resid) + 
fm1_BDat:     (1 + time | Subject_BDat)
         Df    AIC    BIC  logLik  Chisq Chi Df Pr(>Chisq)
fm2_BDat 15 754.30 813.87 -362.15                         
fm1_BDat 16 754.24 817.78 -361.12 2.0564      1     0.1516
	[[alternative HTML version deleted]]

