Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.
Q2: The model to analyse the data is logity=b0+u+b1*trt+b2*I(week>2), but the results for Random effects in R/SPLUS confused me. SAS may be clearer. Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.410637 0.7800511 Which is the random effect 'sigma'? I think it is "1.410637", but what does "0.7800511" mean? That is, i want ot know how to explain/use the above two data for Random effects. Q3:In SAS and other softwares, we can get *p*-values for the random effect 'sigma', but i donot see the *p*-values in the results of R/SPLUS. I have used attributes() to look for them, but no *p* values. Anybody knows how to get *p*-values for the random effect 'sigma',. Any suggestions or help are greatly appreciated. #R Results:MASS' version 7.2-44; R version 2.7.2 library(MASS) summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,family = binomial, data = bacteria)) Linear mixed-effects model fit by maximum likelihood Data: bacteria AIC BIC logLik NA NA NA Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.410637 0.7800511 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ trt + I(week > 2) Value Std.Error DF t-value p-value (Intercept) 3.412014 0.5185033 169 6.580506 0.0000 trtdrug -1.247355 0.6440635 47 -1.936696 0.0588 trtdrug+ -0.754327 0.6453978 47 -1.168779 0.2484 I(week > 2)TRUE -1.607257 0.3583379 169 -4.485311 0.0000 Correlation: (Intr) trtdrg trtdr+ trtdrug -0.598 trtdrug+ -0.571 0.460 I(week > 2)TRUE -0.537 0.047 -0.001 #S-PLUS8.0: The results are the same as R except the followings: AIC BIC logLik 1113.622 1133.984 -550.8111 #SAS9.1.3 proc nlmixed data=b; parms b0=-1 b1=1 b2=1 sigma=0.4; yy=b0+u+b1*trt+b2*week; p=1/(1+exp(-yy)); Model response~binary(p); Random u~normal(0,sigma) subject=id; Run; -2 Log Likelihood = 192.2 AIC (smaller is better)=200.2 AICC (smaller is better) =200.3 BIC (smaller is better)= 207.8 Parameter Estimates Standard Parameter Estimate Error DF t Value Pr > |t| Alpha Lower Upper Gradient b0 3.4966 0.6512 49 5.37 <.0001 0.05 2.1880 4.8052 -4.69E-6 trt -0.6763 0.3352 49 -2.02 0.0491 0.05 -1.3500 -0.00266 -0.00001 I(week>2) -1.6132 0.4785 49 -3.37 0.0015 0.05 -2.5747 -0.6516 -9.35E-7 sigma 1.5301 0.9632 49 1.59 0.1186 0.05 -0.4054 3.4656 -2.42E-6 -- With Kind Regards, oooO::::::::: (..)::::::::: :\.(:::Oooo:: ::\_)::(..):: :::::::)./::: ::::::(_/:::: ::::::::::::: [***********************************************************************] ZhiJie Zhang ,PhD Dept.of Epidemiology, School of Public Health,Fudan University Office:Room 443, Building 8 Office Tel./Fax.:+86-21-54237410 Address:No. 138 Yi Xue Yuan Road,Shanghai,China Postcode:200032 Email:[EMAIL PROTECTED] <[EMAIL PROTECTED]> Website: www.statABC.com <http://www.statabc.com/> [***********************************************************************] oooO::::::::: (..)::::::::: :\.(:::Oooo:: ::\_)::(..):: :::::::)./::: ::::::(_/:::: ::::::::::::: [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.