[R-sig-ME] sas to R

[Steve Hong]

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'?

Thanks,

Steve



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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