Hi,
Your last email was excellent and I got it working using the following.
It did the job extremely quickly, more so a commercial piece of software
my university uses!
#This works fine -
Dataset <- read.table("C:/Program Files/R/rw2011/data/miss/model1.dat",
header=TRUE, sep="\t", na.strings="NA", dec=".",strip.white=TRUE)
Dataset$id <- factor(Dataset$id)
attach(Dataset)
names (Dataset)
require (nlme)
fit <- vector(mode="list", 100)
i = 0
for (i in 1:100)
{ Dataset2 <- subset(Dataset, subset= i == runnb)
summary (fit[[i]]<- lme (trans1 ~ Index1 + grp,
random = ~ 1 | id / grp ,
data = Dataset2,
na.action = "na.exclude")
)
i =i +1
}
Strangely enough I was less lucky with LMER.
> for (i in 1:100)
+ { Dataset2 <- subset(Dataset, subset= i == runnb)
+ fit[[i]] <- lmer(trans1 ~ Index1 + grp + (1|id:grp) + (1|id),
Dataset2, na.action = na.exclude)
+ i =i +1
+ }
Error in lmer(trans1 ~ Index1 + grp + (1 | id:grp) + (1 | id), Dataset2,
:
flist[[2]] must be a factor of length 4000
In addition: Warning messages:
1: numerical expression has 4000 elements: only the first used in:
id:grp
2: numerical expression has 4000 elements: only the first used in:
id:grp
>
Many thanks to you all for your assistance on this issue.
Regards
Stephen
________________________________
From: Douglas Bates [mailto:[EMAIL PROTECTED]
Sent: Sun 26/06/2005 17:01
To: Stephen
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Mixed model
On 6/26/05, Stephen <[EMAIL PROTECTED]> 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.
???? ?"? ???? ????
http://mail.nana.co.il
[[alternative HTML version deleted]]
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