[R-sig-ME] sas to R
Steve Hong
emptican at gmail.com
Mon Jun 25 20:12:52 CEST 2012
I restarted R and tried to fit the model. However, I got the same
message... So, I checked the session information with sessionInfo().
I found there is still nlme package in "loaded via a namespace (and
not attached)". Is it still causing problem?
Here is what I did:
R version 2.15.0 (2012-03-30)
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i386-pc-mingw32/i386 (32-bit)
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[Previously saved workspace restored]
> df <- bcwrear1
> y <- df$day10
> trial <- df$trial
> block <- df$block
> trt <- df$turf
> library(lme4)
Loading required package: Matrix
Loading required package: lattice
Attaching package: ‘lme4’
The following object(s) are masked from ‘package:stats’:
AIC, BIC
> fm.lmer <- lmer(log10(day10) ~ 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
>
> sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lme4_0.999375-42 Matrix_1.0-6 lattice_0.20-6
loaded via a namespace (and not attached):
[1] grid_2.15.0 nlme_3.1-103 stats4_2.15.0
Thanks!!!
Steve
On Mon, Jun 25, 2012 at 1:01 PM, Douglas Bates <bates at stat.wisc.edu> wrote:
> 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
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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