[R-sig-ME] lmer vs SAS results
Beth Holbrook
bvholbi at yahoo.com
Tue Nov 23 22:11:16 CET 2010
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
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