lmer & mcmcsamp bug?

Hi all,

Yesterday I noticed what I take to be a bug in the current version of lme4,
and the folks over at the R-lang forum suggested checking in about it here.

If I create a mer object using lmer, use it as an argument for mcmcsamp
(sampling > 1 times) and then examine my mer object again, I notice a rather
peculiar thing. In particular, all of the variance/standard error terms
change, as do the associated t values for the fixed effects. The estimated
coefficients are unaffected. I figure this is a bug, because I can't see any
reason why mcmcsamp would want to do this. I took a look through the code
for mcmcsamp, but I don't speak C and nothing jumped out at me. Certainly
the function looks like its only meant to return a mermcmc object, so I have
no idea why its messing with my mer.

I've got this same result on a couple of different computers (all macs). It
does, however, seem to be specific to either the version of lmer (
0.999375-28) or of R (2.8.1) that I'm using. I tried it on an old PC version
of R (2.5.1) using lme4 version 0.99875-9, and the same problems don't
happen then. I've included the output from both the PC and mac versions
below.

Sage advice, comments, or confirmation that this is a known bug (I couldn't
find mention of it elsewhere) would be welcome.

########
#
#PC Code & output - R v. 2.5.1 & lme4 v. 0.99875-9.
#
?(fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy))
#Linear mixed-effects model fit by REML
#Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
# ? Data: sleepstudy
# ?AIC ?BIC logLik MLdeviance REMLdeviance
# 1752 1764 -871.8 ? ? ? 1752 ? ? ? ? 1744
#Random effects:
# Groups ? Name ? ? ? ?Variance Std.Dev.
# Subject ?(Intercept) 627.508 ?25.0501
# Subject ?Days ? ? ? ? 35.858 ? 5.9881
# Residual ? ? ? ? ? ? 653.590 ?25.5654
#number of obs: 180, groups: Subject, 18; Subject, 18

#Fixed effects:
?# ? ? ? ? ? Estimate Std. Error t value
#(Intercept) ?251.405 ? ? ?6.885 ? 36.51
#Days ? ? ? ? ?10.467 ? ? ?1.560 ? ?6.71

#Correlation of Fixed Effects:
# ? ? (Intr)
#Days -0.184

?fm1 ?-> fm1.old
?samp0 <- mcmcsamp(fm1, n = 1000)
?fm1
#Linear mixed-effects model fit by REML
#Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
# ? Data: sleepstudy
# ?AIC ?BIC logLik MLdeviance REMLdeviance
# 1752 1764 -871.8 ? ? ? 1752 ? ? ? ? 1744
#Random effects:
# Groups ? Name ? ? ? ?Variance Std.Dev.
# Subject ?(Intercept) 627.508 ?25.0501
# Subject ?Days ? ? ? ? 35.858 ? 5.9881
# Residual ? ? ? ? ? ? 653.590 ?25.5654
#number of obs: 180, groups: Subject, 18; Subject, 18

#Fixed effects:
# ? ? ? ? ? ?Estimate Std. Error t value
#(Intercept) ?251.405 ? ? ?6.885 ? 36.51
#Days ? ? ? ? ?10.467 ? ? ?1.560 ? ?6.71

#Correlation of Fixed Effects:
# ? ? (Intr)
#Days -0.184

#
# As you can see, the estimates don't change here
#
###########


#########
#
# Mac R v. 2.8.1, ?lme4? version 0.999375-28
#
# I make two mers, which should be identical (I make two because there are
some assignment weirdnesses going on here too, #which I haven't yet
understood)

(fm1.to_mcmc <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject),
sleepstudy))

#Linear mixed model fit by REML
#Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
# ? Data: sleepstudy
# ?AIC ?BIC logLik deviance REMLdev
# 1754 1770 -871.8 ? ? 1752 ? ?1744
#Random effects:
# Groups ? Name ? ? ? ?Variance Std.Dev.
# Subject ?(Intercept) 627.568 ?25.0513
# Subject ?Days ? ? ? ? 35.858 ? 5.9882
# Residual ? ? ? ? ? ? 653.584 ?25.5653
#Number of obs: 180, groups: Subject, 18

#Fixed effects:
# ? ? ? ? ? ?Estimate Std. Error t value
#(Intercept) ?251.405 ? ? ?6.885 ? 36.51
#Days ? ? ? ? ?10.467 ? ? ?1.559 ? ?6.71

#Correlation of Fixed Effects:
# ? ? (Intr)
#Days -0.184


(fm1.not_to_mcmc <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject),
sleepstudy))

#Linear mixed model fit by REML
#Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
# ? Data: sleepstudy
# ?AIC ?BIC logLik deviance REMLdev
# 1754 1770 -871.8 ? ? 1752 ? ?1744
#Random effects:
# Groups ? Name ? ? ? ?Variance Std.Dev.
# Subject ?(Intercept) 627.568 ?25.0513
# Subject ?Days ? ? ? ? 35.858 ? 5.9882
# Residual ? ? ? ? ? ? 653.584 ?25.5653
#Number of obs: 180, groups: Subject, 18

#Fixed effects:
# ? ? ? ? ? ?Estimate Std. Error t value
#(Intercept) ?251.405 ? ? ?6.885 ? 36.51
#Days ? ? ? ? ?10.467 ? ? ?1.559 ? ?6.71

#Correlation of Fixed Effects:
# ? ? (Intr)
#Days -0.184


?samp0 <- mcmcsamp(fm1.to_mcmc, n = 1000)
?fm1.to_mcmc

#Linear mixed model fit by REML
#Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
# ? Data: sleepstudy
# ?AIC ?BIC logLik deviance REMLdev
# 1763 1779 -876.7 ? ? 1761 ? ?1753
#Random effects:
# Groups ? Name ? ? ? ?Variance Std.Dev.
# Subject ?(Intercept) 868.398 ?29.469
# Subject ?Days ? ? ? ? 49.619 ? 7.044
?#Residual ? ? ? ? ? ? 904.398 ?30.073
#Number of obs: 180, groups: Subject, 18

#Fixed effects:
# ? ? ? ? ? ?Estimate Std. Error t value
#(Intercept) ?251.405 ? ? ?5.260 ? 47.79
#Days ? ? ? ? ?10.467 ? ? ?1.518 ? ?6.90

# All the variances etc are different from before

#Correlation of Fixed Effects:
# ? ? (Intr)
#Days -0.343

fm1.not_to_mcmc

#Linear mixed model fit by REML
#Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
# ? Data: sleepstudy
# ?AIC ?BIC logLik deviance REMLdev
# 1754 1770 -871.8 ? ? 1752 ? ?1744
#Random effects:
# Groups ? Name ? ? ? ?Variance Std.Dev.
# Subject ?(Intercept) 627.568 ?25.0513
# Subject ?Days ? ? ? ? 35.858 ? 5.9882
# Residual ? ? ? ? ? ? 653.584 ?25.5653
#Number of obs: 180, groups: Subject, 18

#Fixed effects:
# ? ? ? ? ? ?Estimate Std. Error t value
#(Intercept) ?251.405 ? ? ?6.885 ? 36.51
#Days ? ? ? ? ?10.467 ? ? ?1.559 ? ?6.71

# Variances are unaffected here

#Correlation of Fixed Effects:
# ? ? (Intr)
#Days -0.184