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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > Namens Taro Miyagawa > Verzonden: woensdag 1 juni 2011 7:58 > Aan: r-help@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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
