[R] Mixed model

[Douglas Bates]

On 6/26/05, Stephen <szlevine at nana.co.il> wrote:
> 
> 
> 
> Hi All,
> 
> 
> 
> I am currently conducting a mixed model. I have 7 repeated measures on a
> simulated clinical trial. If I understand the model correctly, the
> outcome is the measure (as a factor) the predictors are clinical group
> and trial (1-7). The fixed factors are the measure and group. The random
> factors are the intercept and id and group.
> 
> 
> 
> I tried using 2 functions to calculate mixed effects.
> 
> Following previous correspondence .
> 
> 
> 
> Dataset <- read.table("C:/Program Files/R/rw2011/data/miss/model1a.dat",
> header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
> 
> attach(Dataset)
> 
> 
> 
> require (nlme)
> 
> with(Dataset, table(runnb, id, grp))
> 
> b.lvls <- table(Dataset$runnb)
> 
> nb <- length(b.lvls)
> 
> fit <- vector(mode="list", nb)
> 
> 
> 
> for(i in 1:nb)
> 
>  fit[[i]]<- lme (trans1 ~ Index1 + grp,
> 
>             random = ~ 1 | id / grp ,
> 
>             data = Dataset,
> 
>             na.action = "na.exclude")
> 
> 
> 
> 
> 
> This (above) worked OK only I am having memory problems.
> 
> I have a gig of RAM set at --sdi --max-mem-size=512M (complete version
> below)
> 
> I am wondering if running the file as a database be slower / faster?
> 
> 
> 
> Then I read that lme4 does it quicker and more accurately
> 
> so I thought that I should re-run the code but from the for line:
> 
> 
> 
> > for (i in 1:nb)
> 
> +  fit[[i]]  <- lmer(trans1 ~ Index1 + grp + (1|id:grp) + (1|id),
> 
> + Dataset, na.action = na.exclude)
> 
> 
> 
> Producing
> 
> 
> 
> Error in lmer(trans1 ~ Index1 + grp + (1 | id:grp) + (1 | id), Dataset,
> :
> 
>         flist[[2]] must be a factor of length 200000
> 
> In addition: Warning messages:
> 
> 1: numerical expression has 200000 elements: only the first used in:
> id:grp
> 
> 2: numerical expression has 200000 elements: only the first used in:
> id:grp

Check

str(Dataset)

and, if necessary, convert id to a factor with

Dataset$id <- factor(Dataset$id)


In is not surprising that you are running into memory problems.  Look
at the size of one of the fitted objects from lme or from lmer.  They
are very large because they contain a copy of the model frame (the
parts of Dataset that are needed to evaluate the model) plus a lot of
other information.  You have a large Dataset and you are saving
multiple copies of it although I must admit that I don't understand
why the calls to lme or lmer are in a loop.