[R-sig-ME] sas to R

[Douglas Bates]

On Mon, Jun 25, 2012 at 12:41 PM, Steve Hong <emptican at gmail.com> wrote:
> Hi Prof. Bates,
>
> Thanks for replying.  Yes, I loaded both packages together and ran
> them.  I don't understand different/separate R 'session'?  Obviously,
> it seems not different versions (e.g., 2.15.0 vs. 2.14.0).  Could you
> rephrase what you meant by R 'session'?

I mean to run R, load lme4 and fit the model.  Then quit R and restart
it, load nlme and fit that model.

> On Mon, Jun 25, 2012 at 12:28 PM, Douglas Bates <bates at stat.wisc.edu> wrote:
>> Are you trying to load both the nlme and the lme4 packages at the same
>> time?  That can cause problems.  You are better off fitting the lmer
>> model in one R session and the lme model in another.
>>
>> On Mon, Jun 25, 2012 at 11:50 AM, Steve Hong <emptican at gmail.com> wrote:
>>> Thank all of you for replying to me.
>>>
>>> I tried lmer, lme, and SAS.  I was able to get outputs when I use
>>> 'lme' whereas no results from 'lmer'.  I don't know why.  Does anyone
>>> know what the warning message mean?  Outputs from  'lme' were similar
>>> with those from SAS.  Below is selected outputs from lmer, lme, and
>>> SAS, FYI.
>>>
>>> Thanks again,
>>>
>>> Steve Hong
>>>
>>>> fm.lmer <- lmer(y ~ trt + (1|trial/block/trt), data=df, na.action=na.omit)
>>> Error: length(f1) == length(f2) is not TRUE
>>> In addition: Warning messages:
>>> 1: In block:trial :
>>>   numerical expression has 92 elements: only the first used
>>> 2: In block:trial :
>>>   numerical expression has 92 elements: only the first used
>>> 3: In trt:(block:trial) :
>>>   numerical expression has 92 elements: only the first used
>>> 4: In block:trial :
>>>   numerical expression has 92 elements: only the first used
>>> 5: In block:trial :
>>>   numerical expression has 92 elements: only the first used
>>>> fm.lme <- lme(y ~ trt, random=(~1|trial/block/trt), data = df, na.action=na.omit)
>>>> summary(fm.lme)
>>> Linear mixed-effects model fit by REML
>>>  Data: df
>>>         AIC       BIC   logLik
>>>   -85.22388 -60.68041 52.61194
>>>
>>> Random effects:
>>>  Formula: ~1 | trial
>>>         (Intercept)
>>> StdDev:   0.1112442
>>>
>>>  Formula: ~1 | block %in% trial
>>>          (Intercept)
>>> StdDev: 1.449228e-06
>>>
>>>  Formula: ~1 | trt %in% block %in% trial
>>>         (Intercept)  Residual
>>> StdDev:  0.07081356 0.1020226
>>>
>>> Fixed effects: y ~ trt
>>>                   Value  Std.Error DF    t-value p-value
>>> (Intercept)  0.24428523 0.08793775 56  2.7779337  0.0074
>>> trtau2      -0.00996643 0.05605221 25 -0.1778063  0.8603
>>> trtberm     -0.12786905 0.05686903 25 -2.2484830  0.0336
>>> trtls44      0.12326637 0.05478364 25  2.2500582  0.0335
>>> trtsr10y5    0.02513355 0.05517460 25  0.4555275  0.6527
>>> trtsr10y6    0.01932992 0.05478364 25  0.3528410  0.7272
>>>  Correlation:
>>>           (Intr) trtau2 trtbrm trtl44 trt105
>>> trtau2    -0.314
>>> trtberm   -0.309  0.486
>>> trtls44   -0.321  0.504  0.497
>>> trtsr10y5 -0.319  0.500  0.493  0.511
>>> trtsr10y6 -0.321  0.504  0.497  0.515  0.511
>>>
>>> Standardized Within-Group Residuals:
>>>           Min            Q1           Med            Q3           Max
>>> -2.614096e+00 -5.666986e-01 -9.727356e-05  4.692685e-01  2.410879e+00
>>>
>>> Number of Observations: 92
>>> Number of Groups:
>>>                     trial          block %in% trial trt %in% block %in% trial
>>>                         2                         6                        36
>>>> anova(fm.lme)
>>>             numDF denDF  F-value p-value
>>> (Intercept)     1    56 9.907983  0.0026
>>> trt             5    25 4.122070  0.0072
>>>
>>>
>>> SAS code and outputs:
>>> proc glimmix data=df;
>>> model y=trt;
>>> random trial block(trial) turf(block*turf);
>>> run;
>>>
>>>     Covariance Parameter Estimates
>>>
>>>                                 Standard
>>> Cov Parm             Estimate       Error
>>>
>>> trial                 0.01237     0.01823
>>> block(trial)                0           .
>>> trt(trial*block)    0.005015    0.002546
>>> Residual              0.01041    0.001963
>>>
>>>
>>>        Type III Tests of Fixed Effects
>>>
>>>              Num      Den
>>> Effect         DF       DF    F Value    Pr > F
>>>
>>> trt            5       25       4.12    0.0072
>>>
>>>
>>>
>>> On Mon, Jun 25, 2012 at 10:25 AM, Kevin Wright <kw.stat at gmail.com> wrote:
>>>>
>>>> This could be similar to a multi-location RCB design were "trial" is
>>>> location.  Since no distribution is specified, the distribution is
>>>> assumed to be Gaussian.  Make sure that trial, block, trt are factors,
>>>> this should be similar to SAS:
>>>>
>>>> lmer(y ~ trt + (1|trial/block/trt), data=df)
>>>>
>>>> > proc glimmix data=df;
>>>> > class trial block trt;
>>>> > model y=trt;
>>>> > random trial block(trial) trt(block*trial);
>>>>
>>>> Kevin Wright
>>>
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