Message-ID: <AA818EAD2576BC488B4F623941DA74273B789AE1@inbomail.inbo.be>
Date: 2011-06-01T08:11:38Z
From: ONKELINX, Thierry
Subject: different results from lme() and lmer()
In-Reply-To: <SNT131-w364FB531093A14FDDFC3BAF7D0@phx.gbl>
Dear Miya,
Notice the very strong negative correlation between the random intercept and the random slope in the lme() model. That is usually an indication of problems (in this case overfitting). If you drop the random slope, then both models yield the same parameters.
Plotting the data reviels a much better model specification.
dat <- read.table(file = "http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt", header = TRUE)
library(ggplot2)
ggplot(dat, aes(x = week, y = root, group = plant, colour = fertilizer)) + geom_line()
lmer(root~fertilizer + week +(1|plant),data=dat)
Best regards,
Thierry
PS Use R-sig-mixedmodels for questions on mixed models
> -----Oorspronkelijk bericht-----
> Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> Namens Taro Miyagawa
> Verzonden: woensdag 1 juni 2011 7:58
> Aan: r-help at r-project.org
> Onderwerp: [R] different results from lme() and lmer()
>
>
> Hello R-help,
> I'm studying an example in the R book.
> The data file is available from the link
> below.http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt
> Could you explain Why the results from lme() and lmer() are different in the
> following case? In other examples, I can get the same results using the two
> functions, but not here...
> Thank you.Miya
>
> library(lme4)library(nlme)# object dat contains the data
> > summary(lme(root~fertilizer,random=~week|plant,data=dat))Linear mixed-
> effects model fit by REML?Data: dat?? ? ? ?AIC ? ? ?BIC ? ?logLik? 171.0236 183.3863 -
> 79.51181
> Random effects:?Formula: ~week | plant?Structure: General positive-definite,
> Log-Cholesky parametrization? ? ? ? ? ? StdDev ? ?Corr ?(Intercept) 2.8639832
> (Intr)week ? ? ? ?0.9369412 -0.999Residual ? ?0.4966308
> Fixed effects: root ~ fertilizer?? ? ? ? ? ? ? ? ? ? ? Value Std.Error DF ? t-value p-
> value(Intercept) ? ? ? ?2.799710 0.1438367 48 19.464499 ? 0e+00fertilizercontrol -
> 1.039383 0.2034158 10 -5.109645 ? 5e-
> 04?Correlation:?? ? ? ? ? ? ? ? ? (Intr)fertilizercontrol -0.707
> Standardized Within-Group Residuals:? ? ? ?Min ? ? ? ? Q1 ? ? ? ?Med ? ? ? ? Q3 ? ? ? ?Max?-
> 1.9928118 -0.6586834 -0.1004301 ?0.6949714 ?2.0225381
> Number of Observations: 60Number of Groups: 12
>
> > lmer(root~fertilizer+(week|plant),data=dat)Linear mixed model fit by
> REML?Formula: root ~ fertilizer + (week | plant)?? ?Data: dat?? ?AIC BIC logLik
> deviance REMLdev?174.4 187 -81.21 ? ?159.7 ? 162.4Random
> effects:?Groups ? Name ? ? ? ?Variance ? Std.Dev. ? Corr ??plant ? ?(Intercept)
> 4.1416e-18 2.0351e-09 ? ? ??? ? ? ? ? week ? ? ? ?8.7452e-01 9.3516e-01
> 0.000??Residual ? ? ? ? ? ? 2.2457e-01 4.7389e-01 ? ? ??Number of obs: 60, groups:
> plant, 12
> Fixed effects:? ? ? ? ? ? ? ? ? Estimate Std. Error t value(Intercept) ? ? ? ?-
> 0.1847 ? ? 0.2024 ?-0.913fertilizercontrol ?-0.7612 ? ? 0.2862 ?-2.660
> Correlation of Fixed Effects:? ? ? ? ? ? (Intr)frtlzrcntrl -0.707
>
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