lmer vs SAS results

When I posted the following question, my data was formatted incorrectly.  Here 
is the correct dataset:


Thanks,
Beth

522071 2.115943177 N 0.4 
522071 2.470557485 Y 0.4 
522072 1.902546779 Y 0.4 
522072 2.272769587 Y 0.4 
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525071 2.314709693 Y 0.4 
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525071 1.068185862 Y 0.4 
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629077 1.685741739 Y 1965 



------------------------------

Message: 3
Date: Tue, 23 Nov 2010 13:11:16 -0800 (PST)

To: r-sig-mixed-models at r-project.org

Subject: [R-sig-ME] lmer vs SAS results
Message-ID: <346807.26639.qm at web35303.mail.mud.yahoo.com>
Content-Type: text/plain; charset=us-ascii

First, let me preface this by saying I am an ecology Ph.D. student, relatively 
new to R, and even more new to mixed models, so I apologize in advance for my 
limited knowledge on this topic. (Also, I need to apologize for the length of 
this email - but I wanted to be as specific as possible).

I posted a question a few weeks ago, and received many helpful responses, so I'm 


hoping you can help me again.  The statistician on my committee has assisted me 
in setting up my analysis design; however, he is receiving different results 
using SAS than I am using lmer.  I understand that the goal of lmer is not to 
replicate results of SAS; however, I am unable to move ahead with my 
dissertation until I can provide him with a satisfactory explanation for these 
differences (I no longer have access to SAS so I need to use R as my primary 
statistical software).

My analysis design is to run a mixed model to test for the effects of prey 
movement ("preyswimy.n") and light ("lightclass") on the reaction distance 
("logrxndist") of young lake trout.  The random effect is "trial" since I 
observed several responses to prey for the same fish during each trial.

Using the lme4 package, I set up the following:

options(contrasts = c(factor = "contr.SAS", ordered = "contr.poly"))
lightclass <- factor(Dataset$light)
trial <- factor(Dataset$trial)
model1 <-lmer(logrxndist~preyswimy.n * lightclass + (1|trial), Dataset, 
REML=FALSE)
model2 <-lmer(logrxndist~preyswimy.n + lightclass + (1|trial), Dataset, 
REML=FALSE)
model3 <-lmer(logrxndist~preyswimy.n + (1|trial), Dataset, REML=FALSE)

Based on the following results, I concluded that the most appropriate model was 
model3 where lightclass was omitted.

anova(model1, model2, model3)

Data: Dataset
Models:
model3: logrxndist ~ preyswimy.n + (1 | trial)
model2: logrxndist ~ preyswimy.n + lightclass + (1 | trial)
model1: logrxndist ~ preyswimy.n * lightclass + (1 | trial)
       Df    AIC    BIC   logLik  Chisq Chi Df Pr(>Chisq)
model3  4 214.21 229.98 -103.103                        
model2  9 215.26 250.74  -98.628 8.9494      5     0.1111
model1 14 219.15 274.35  -95.575 6.1062      5     0.2960  



In SAS, the statistician on my committee used the following code:


model1: 
proc mixed data=holbrook.bethfull method=ml;
  class preyswimy.n trial light;
  model logrxndist = light | preyswimy.n /solution;
  random trial;
  run;

model2:
proc mixed data=holbrook.bethfull method=ml;
  class preyswimy.n trial light;
  model logrxndist = light  preyswimy.n / solution;
  random trial;
  run;


model3
proc mixed data=holbrook.bethfull method=ml;
  class preyswimy.n trial;
  model logrxndist = preyswimy.n / solution ;
  random trial;
  run;

His results are as follows:
    
               AIC    BIC    -2 Log Likelihood  
model1   216.7  251.1   188.7 
model2   213.3  235.4   195.3
model3   214.2  224.0   206.2                        

Based on these results, he concluded that model2 had the lowest AIC and light 
was significant.

The lmer and SAS fixed effect parameter estimates were similar for model1, 
model2, and model3 (see below).  The lmer and SAS variance estimates for the 
random effect were the same for model3 (when light was excluded).  However, the 
variance estimates for the random effect differed in model1 and in model2.  The 
lmer results suggest that there is no random effect of "trial" in these models. 
However, SAS results gave a larger variance estimate for the random effect of 
trial in model1 and model2.  Any ideas why that might be the case?  
    
model1 Random effects
Groups   Variance-lmerVariance-SAS  
trials    1.0305e-11 0.005385
Residual        9.6697e-02 0.09126

model1 Fixed effects
                            Estimate-lmer   Estimate-SAS
(Intercept)                 2.151398   2.1448
preyswimy.nN               -0.344458   -0.3386
lightclass0.4               0.056317   0.06450
lightclass2.5               0.104469   0.1108
lightclass8                 0.074183   0.07667
lightclass55               -0.043046  -0.03805
lightclass340              -0.074705   -0.06529
preyswimy.nN:lightclass0.4 -0.257426   -0.2651
preyswimy.nN:lightclass2.5 -0.179126  -0.1808
preyswimy.nN:lightclass8   -0.058160   -0.05099
preyswimy.nN:lightclass55  -0.007468   -0.00753
preyswimy.nN:lightclass340 -0.123571   -0.1413


model2 Random effects:
GroupsVariance-lmerVariance-SAS
trial8.3902e-120.004775
Residual9.8259e-02  0.09394

model2 Fixed effects:

               Estimate-lmerEstimate-SAS
(Intercept)     2.20135   2.1975
preyswimy.nY    -0.45319   -0.4507
lightclass0.4  -0.02280-0.01755
lightclass2.5   0.02251    0.02634
lightclass8     0.04108    0.04401
lightclass55   -0.03539   -0.03229
lightclass340  -0.13272    -0.1336



model3 Random effects:
GroupsVariance-lmerVariance-SAS
trial0.00769170.007692
Residual0.0939409  0.09394

model3 Fixed effects:
  Estimate-lmerEstimate-SAS
(Intercept)2.186092.1861
preyswimy.nN  -0.46016 -0.4602


Thank you for any insight that you can provide.  Below, I've also attached a 
copy of the data in case you would like to run it yourself.

Thanks again,
Beth

Beth Holbrook
Ph.D. Candidate
University of Minnesota


Dataset

triallogrxndistpreyswimy.nlight
5220712.115943177N0.4
5220712.470557485Y0.4
5220721.902546779Y0.4
5220722.272769587Y0.4
5220731.985426474Y0.4
5220731.862131379Y0.4
5220732.275541688Y0.4
5220731.846337112Y0.4
5250712.314709693Y0.4
5250712.360025089Y0.4
5250712.344785123Y0.4
5250711.068185862Y0.4
5250712.540329475Y0.4
5250722.030599722Y0.4
5250721.848804701Y0.4
5250722.326540669Y0.4
5250732.186391216Y0.4
5250732.421603927Y0.4
5250732.381295623Y0.4
5250731.672097858Y0.4
6050761.844477176Y0.4
6050762.219060332Y0.4
6050762.518645524Y0.4
6050762.546419267Y0.4
6060712.153814864Y0.4
6060712.625106575Y0.4
6060712.369957607Y0.4
6060711.589949601N0.4
6060712.506505032Y0.4
6060721.439332694N0.4
6060721.822168079Y0.4
6220732.218010043Y0.4
6220732.299289334Y0.4
6220741.431363764N0.4
6220742.419955748Y0.4
6220752.227372442Y0.4
6220752.338057875Y0.4
6220761.643452676N0.4
6220762.260309946Y0.4
6220761.404833717N0.4
6220762.343802333Y0.4
6250712.366236124Y0.4
6250712.557867962Y0.4
6250712.452706227Y0.4
6250721.920645001Y0.4
6250721.967079734Y0.4
6250721.6599162N0.4
6250721.492760389N0.4
6250721.240549248N0.4
6250722.040206628N0.4
6250722.428134794Y0.4
5220741.860936621Y2.5
5220742.020361283N2.5
5220742.567731963Y2.5
5220751.599883072N2.5
5220761.350248018N2.5
5220762.288919606Y2.5
5220761.320146286N2.5
5230712.069668097N2.5
5230711.953759692N2.5
5230712.016197354N2.5
5230712.240549248Y2.5
5250741.620136055N2.5
5250742.160768562N2.5
5250741.498310554N2.5
5250742.431685345N2.5
5250751.855519156N2.5
6070712.357363031Y2.5
6070712.657629431Y2.5
6070712.302763708Y2.5
6070712.353723938Y2.5
6070712.584670384Y2.5
6070722.393224116Y2.5
6070721.722633923N2.5
6070721.830588669N2.5
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