lmer give error message

Fri, Oct 8, 2010 3:07 PM

Hi John,

pid     visit       eye         y 
"integer" "integer"  "factor" "integer" 

Part of the issue is that the modeling machinery is set up
to work with factor variables.  lmer does not always convert
numerics to integers.  Set up factor versions of your
independent variable groups.

Linear mixed model fit by REML 
Formula: y ~ 1 + (1 | fpid/fvisit) + (1 | fpid/eye) 
   Data: dat 
  AIC  BIC logLik deviance REMLdev
 2848 2875  -1418     2835    2836
Random effects:
 Groups      Name        Variance Std.Dev.
 fvisit:fpid (Intercept) 0.92510  0.96182 
 eye:fpid    (Intercept) 0.51945  0.72073 
 fpid        (Intercept) 4.02193  2.00547 
 fpid        (Intercept) 4.02191  2.00547 
 Residual                0.89761  0.94742 
Number of obs: 718, groups: fvisit:fpid, 359; eye:fpid, 240; fpid, 120

Fixed effects:
            Estimate Std. Error t value
(Intercept)   4.0648     0.2702   15.04

Error in function (fr, FL, start, REML, verbose)  : 
  Number of levels of a grouping factor for the random effects
must be less than the number of observations

Issues of model specification, number of observations
per group, and groups missing data remain
to be worked out.

, , eye = L

     fvisit
fpid  0 1 2
  1   1 1 1
  2   1 1 1
  3   1 1 1
...
  37  1 1 1
  38  1 0 1
  39  1 1 1
...
  119 1 1 1
  120 1 1 1

, , eye = R

     fvisit
fpid  0 1 2
  1   1 1 1
  2   1 1 1
  3   1 1 1
  4   1 1 1
  5   1 1 1
...
  117 1 1 1
  118 1 1 1
  119 1 1 1
  120 1 1 1



Steven McKinney

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On
Behalf Of array chip
Sent: October-08-10 2:48 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] lmer give error message

Hi, I have an ophthalmology dataset, that has as response variable "y", patient
ID (pid), visit (0,1,2) and eye examined ("L' and "R").

Basically, each patient has 3 visit, during each visit, both eyes will be
assessed and produce response measurements. The first question is should I treat
eye.examined and visit as crossed or nested? I think it should be treated as
crossed because each eye will be assessed in each visit. On the other hand, the
2 measurements from the 2 eyes were from a given visit, so it looks like it can
be treated as nested as well (eye %in% visit).

The second question is, no matter which way I use, lmer() gives me error
message, while lme() from nlme package can handle nested design easily. Also, is
there a way to specify corssed design in lme() as well?

dat <- read.table ("dat.txt", sep='\t', row.names=1, header=T)
library(nlme)

    ### nested using lme

lme(y~1, random=~1|pid/visit/eye,dat)

Random effects:
 Formula: ~1 | pid
        (Intercept)
StdDev:    2.881611

 Formula: ~1 | visit %in% pid
        (Intercept)
StdDev:   0.8154632

 Formula: ~1 | eye %in% visit %in% pid
        (Intercept)  Residual
StdDev:    1.065172 0.5321993

    ### nested using lmer

lmer(y~1+(1|pid/visit/eye),dat)

Error: length(f1) == length(f2) is not TRUE
In addition: Warning messages:
1: In visit:pid :
  numerical expression has 718 elements: only the first used
2: In visit:pid :
  numerical expression has 718 elements: only the first used

    ### crossed

lmer(y~1+(1|pid/visit)+(1|pid/eye),dat)

Error: length(f1) == length(f2) is not TRUE
In addition: Warning messages:
1: In visit:pid :
  numerical expression has 718 elements: only the first used
2: In visit:pid :
  numerical expression has 718 elements: only the first used
3: In eye:pid : numerical expression has 718 elements: only the first used
4: In eye:pid : numerical expression has 718 elements: only the first used


Can anyone share their thoughts.

Thanks

John

lmer give error message

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