Side effects from mcmcsamp()
Hello, I noticed this a wile ago. One thing that occurred to me was to copy to another object before running mcmcsamp, example: obmix0<-obmix where obmix is the lmer object. then do: mcmcsamp(obmix).... For some reason obmix0 gets modified too. It's been a while since I checked this so it may have been fixed. Anyways, a way around this is to extract all the info needed from your lmer object to other variables before running mcmcsamp, that's what I do now and it works fine. Thanks JP
John Maindonald wrote:
Dear Douglas - The following is at the very least a serious trap! The output from VarCorr() and print() changes remarkably after running mcmcsamp() with the lmer object as argument. The estimate of sigma is always (for these data) high, but roughly in the middle of the range of values that come out of ant111b.samp at sigma I have also been able to reproduce this behaviour (a) under lme4_0.999375-28 [below, I use 30] (b) under Mac OSX. In fact, I thought initially that this was an OSX problem! While mcmcsamp() is in view, it would be very helpful to have a progress report on where it is at. Many thanks John Maindonald. I run the following code: library(DAAG) library(lme4) ant111b.lmer <- lmer(harvwt ~ 1 + (1 | site), data=ant111b) ant111b.lmer VarCorr(ant111b.lmer) ant111b.samp <- mcmcsamp(ant111b.lmer, n=1000) ## Now, extract the VarCorr and summary correlations VarCorr(ant111b.lmer) summary(ant111b.lmer)
ant111b.lmer <- lmer(harvwt ~ 1 + (1 | site), data=ant111b) ant111b.lmer
Linear mixed model fit by REML
Formula: harvwt ~ 1 + (1 | site)
Data: ant111b
AIC BIC logLik deviance REMLdev
100.4 104.8 -47.21 95.08 94.42
Random effects:
Groups Name Variance Std.Dev.
site (Intercept) 2.36773 1.53874
Residual 0.57754 0.75996
Number of obs: 32, groups: site, 8
Fixed effects:
Estimate Std. Error t value
(Intercept) 4.2917 0.5603 7.659
library(DAAG) library(lme4) ant111b.lmer <- lmer(harvwt ~ 1 + (1 | site), data=ant111b) ant111b.lmer
Linear mixed model fit by REML
Formula: harvwt ~ 1 + (1 | site)
Data: ant111b
AIC BIC logLik deviance REMLdev
100.4 104.8 -47.21 95.08 94.42
Random effects:
Groups Name Variance Std.Dev.
site (Intercept) 2.36773 1.53874
Residual 0.57754 0.75996
Number of obs: 32, groups: site, 8
Fixed effects:
Estimate Std. Error t value
(Intercept) 4.2917 0.5603 7.659
ant111b.samp <- mcmcsamp(ant111b.lmer, n=1000) VarCorr(ant111b.lmer)
$site
(Intercept)
(Intercept) 8.363436
attr(,"stddev")
(Intercept)
2.891961
attr(,"correlation")
(Intercept)
(Intercept) 1
attr(,"sc")
sigmaREML
1.428289
summary(ant111b.lmer)
Linear mixed model fit by REML
Formula: harvwt ~ 1 + (1 | site)
Data: ant111b
AIC BIC logLik deviance REMLdev
112.1 116.5 -53.04 105.7 106.1
Random effects:
Groups Name Variance Std.Dev.
site (Intercept) 8.3634 2.8920
Residual 2.0400 1.4283
Number of obs: 32, groups: site, 8
Fixed effects:
Estimate Std. Error t value
(Intercept) 4.2917 0.4171 10.29
sessionInfo()
R version 2.9.0 (2009-04-17) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] lme4_0.999375-30 Matrix_0.999375-26 lattice_0.17-22 [4] DAAG_0.99-1 MASS_7.2-47 loaded via a namespace (and not attached): [1] grid_2.9.0 John Maindonald email: john.maindonald at anu.edu.au phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre for Mathematics & Its Applications, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200.
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============================= Juan Pedro Steibel Assistant Professor Statistical Genetics and Genomics Department of Animal Science & Department of Fisheries and Wildlife Michigan State University 1205-I Anthony Hall East Lansing, MI 48824 USA Phone: 1-517-353-5102 E-mail: steibelj at msu.edu