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,

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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/>
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