random effect variance per treatment group in lmer

I'm afraid I don't understand what the expression

rep(1:n.timepoints, n.subj.per.tx*2)*drug

would mean.  It seems that the variable drug is a factor and it
doesn't make sense to multiply a factor by an integer variable.

The easiest way out is to add variables to the dat.new data frame. Say

dat.new$Dind <- as.numeric(dat.new$drug == "D")
dat.new$Pind <- as.numeric(dat.new$drug == "P")

(fm.het <- lmer(dv ~ ... + (0+Dind|Patient)+(0+Pind|Patient))

On 7/13/07, Afshartous, David <afshart at exchange.sba.miami.edu> wrote:

Alan,

Thanks for the suggestion.  I noticed the following error msg
for that lmer call:

( fm.het <- lmer( dv ~ rep(1:n.timepoints, n.subj.per.tx*2)*drug + ( 0

+ as.numeric(drug=="D") | Patient ) + ( 0 + as.numeric(drug=="P") |
Patient ), data=dat.new ) )
Error: length(term) == 3 is not TRUE

I expect that the problem is due to "AsIs" terms in the model frame
not being identified correctly in later function calls that build
model matrices.  Parsing model formulas and building the model frame
and model matrices in general settings is a bit of a black art.

The easiest way out is to add variables to the dat.new data frame. Say

dat.new$Dind <- as.numeric(dat.new$drug == "D")
dat.new$Pind <- as.numeric(dat.new$drug == "P")

(fm.het <- lmer(dv ~ ... + (0+Dind|Patient)+(0+Pind|Patient))

I'm afraid I don't understand what the expression

rep(1:n.timepoints, n.subj.per.tx*2)*drug

would mean.  It seems that the variable drug is a factor and it
doesn't make sense to multiply a factor by an integer variable.

I hope this helps,
Doug Bates

I tried a few changes to the model but the error still exists; I'll
keep checking.  I assume the rationale for the structure of your lmer
call, where you use as.numeric as opposed to just drug above, is to
insure that
you do not introduce a random effect for drug into the model?

Regards,
Dave

-----Original Message-----
From: Alan Cobo-Lewis [mailto:alanc at umit.maine.edu]
Sent: Wednesday, July 11, 2007 6:40 PM
To: r-sig-mixed-models at r-project.org
Cc: " "Afshartous at basalt.its.maine.edu; Afshartous, David;
Andrew Robinson
Subject: Re: random effect variance per treatment group in lmer


Dave,

How about using stratifying variance on level of drug using (
0 + as.numeric(drug=="D") | Patient ) + ( 0 +
as.numeric(drug=="P") | Patient ) Here's some code (whose sim
also builds in a fixed effect of time that applies only to
the Drug condition).

set.seed(500)
n.timepoints <- 8
n.subj.per.tx <- 20
sd.d <- 5; sd.p <- 2; sd.res <- 1.3
drug <- factor(rep(c("D", "P"), each = n.timepoints, times =
n.subj.per.tx)) drug.baseline <- rep( c(0,5),
each=n.timepoints, times=n.subj.per.tx ) Patient <-
rep(1:(n.subj.per.tx*2), each = n.timepoints)
Patient.baseline <- rep( rnorm( n.subj.per.tx*2, sd=c(sd.d,
sd.p) ), each=n.timepoints ) time <- factor(paste("Time-",
rep(1:n.timepoints, n.subj.per.tx*2), sep="")) time.baseline
<- rep(1:n.timepoints,n.subj.per.tx*2)*as.numeric(drug=="D")
dv <- rnorm( n.subj.per.tx*n.timepoints*2,
mean=time.baseline+Patient.baseline+drug.baseline, sd=sd.res
) dat.new <- data.frame(time, drug, dv, Patient) xyplot(
dv~time|drug, group=Patient, type="l", data=dat.new ) # fit
model treats time as a quantitative predictor ( fm.het <-
lmer( dv ~ rep(1:n.timepoints, n.subj.per.tx*2)*drug + ( 0 +
as.numeric(drug=="D") | Patient ) + ( 0 +
as.numeric(drug=="P") | Patient ), data=dat.new ) )


HTH,
alan

From: "Afshartous, David" <afshart at exchange.sba.miami.edu>

 asked:

All,
I didn't receive a response to the query below sent to the general
R-help mailing list so figured I'd try this mailing list.

Apologies in

advance if this is an overly simplistic question for this

list; I just

started w/ lmer after not using lme for awhile.
Cheers,
Dave

depending on

i, i.e,
var(b_ij) = tau_i.

Didn't see this in the docs or Pinherio & Bates (section 5.2 is
specific for modelling within group errors).  Sample

repeated measures

code below is for a single random effect variance, where the random
effect corresponds to patient.
cheers,
dave


z <- rnorm(24, mean=0, sd=1)
time <- factor(paste("Time-", rep(1:6, 4), sep="")) Patient

<- rep(1:4,

each = 6) drug <- factor(rep(c("D", "P"), each = 6, times =

2)) ## P =

placebo, D = Drug dat.new <- data.frame(time, drug, z,

Patient) fm =

lmer(z ~ drug + time + (1 | Patient), data = dat.new )

--
Alan B. Cobo-Lewis, Ph.D.             (207) 581-3840 tel
Department of Psychology              (207) 581-6128 fax
University of Maine
Orono, ME 04469-5742                  alanc at maine.edu

http://www.umaine.edu/visualperception