Thanks. I will find and see the recent discussion on this list about
significance of random effects.
On Sun, Dec 7, 2008 at 11:14 AM, Ken Beath wrote:
> On 07/12/2008, at 12:34 PM, zhijie zhang wrote:
>
> Dear Rusers,
>> Sorry for re-post my question in this list. Some person has recommend
>> that this list is more specific for me.
>> 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.
>>
>>
> PQL is not maximum likelihood (it is an approximation which uses lme
> internally and this is what generates the results) so the results should be
> NA. The R and S-Plus versions have obviously diverged.
>
> SAS nlmixed uses maximum likelihood so a log likelihood is available.
>
> 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.
>>
>>
> I wonder if in PQL these have any real meaning, as they are obtained from
> the linear mixed effects step. Try using lmer in the lme4 package.
>
> 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',.
>>
>
> The standard answer is of the form "Just because SAS has it, doesn't mean
> it is a good idea". There was a recent discussion on this list about
> significance of random effects.
>
> Ken
>
>
>
>> 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:epistat@gmail.com <
>> Email%3Aepistat@gmail.com >
>> Website: www.statABC.com
>> [***********************************************************************]
>> oooO:::::::::
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>>
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>>
>> _______________________________________________
>> R-sig-mixed-models@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
--
With Kind Regards,
oooO:::::::::
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:\.(:::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:epistat@gmail.com
Website: www.statABC.com
[***********************************************************************]
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:\.(:::Oooo::
::\_)::(..)::
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