Another case of -1.0 correlation of random effects
Kevin E. Thorpe wrote:
Ben Bolker wrote:
Ken Knoblauch wrote:
Kevin E. Thorpe <kevin.thorpe at ...> writes:
My data come from a crossover trial and are balanced.
> str(gluc)
'data.frame': 96 obs. of 4 variables: $ Subject : int 1 2 3 5 6 7 10 11 12 13 ... $ Treatment: Factor w/ 2 levels "Barley","Oat": 1 1 1 1 1 1 1 1 1 1 ... $ Dose : int 8 8 8 8 8 8 8 8 8 8 ... $ iAUC : num 110 256 129 207 244 ... clip>
Shouldn't you make Subject into a factor? Ken
It would make the plot a little bit prettier but I don't think it matters in this case because variable that appears as a grouping variable (i.e. on the right of the | ) is automatically treated as a factor? I think? Since it is really a crossover trial, it would seem reasonable in principle to have the (Treatment|Subject) random effect in there as well. I'm not sure what to do about the -1 correlation: it seems the choices (not necessarily in order) are (1) throw up your hands and say there's not enough data to estimate independently; (2) try WinBUGS, possibly with slightly informative priors; (3) try using lme4a to create profiles of the parameters and see if you can figure out what's happening.
Let's see. I wish (1) was an option. (2) would be promising if my knowledge of BUGS and Bayesian methods filled more than a thimble. Thanks to Jarrod for his suggestion in response to this. I'll take a look at that too. Option (3) is probably worth a go too. Aside from the fact that the Dose variable are the actual doses and not categories, and we all know not to categorize continuous variables, what are your thoughts on treating Dose as a factor (since it seems to behave)? Thanks all for taking the time to provide your suggestions. Kevin
Okay, I now have lme4a installed and I get an error message when I do
(note: this is the same model from my OP):
> gluc.lmer1a <-
lmer(iAUC~Dose+(Dose|Subject),data=gluc,subset=Treatment=="Oat",REML=FALSE)
> gluc.lmer1a
Linear mixed model fit by maximum likelihood ['lmer']
Formula: iAUC ~ Dose + (Dose | Subject)
Data: gluc
Subset: Treatment == "Oat"
AIC BIC logLik deviance
575.1 586.3 -281.6 563.1
Random effects:
Groups Name Variance Std.Dev. Corr
Subject (Intercept) 7492.19 86.557
Dose 14.68 3.831 -1.000
Residual 4727.27 68.755
Number of obs: 48, groups: Subject, 12
Fixed effects:
Estimate Std. Error t value
(Intercept) 309.352 29.338 10.544
Dose -14.424 3.533 -4.083
Correlation of Fixed Effects:
(Intr)
Dose -0.647
> pr1 <- profile(gluc.lmer1a at env) ## using @env base on other threads
Error in x[ndat + (1L:deg) - deg] :
only 0's may be mixed with negative subscripts
Is this because I'm trying to profile a model that profile() cannot
handle yet, or does it indicate there really are serious problems with
my model?
I'm at a loss as to how determine what is really going on with these data.
Kevin
Kevin E. Thorpe Biostatistician/Trialist, Knowledge Translation Program Assistant Professor, Dalla Lana School of Public Health University of Toronto email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016