Differing variable lengths (missing data) and Model errors in lmer()

Dear All,

I am trying to run a nested random effects model in lmer (for R 2.9.1, 
lme4 version 0.999375-31 ) using data which is structured as follows:

family offsp. P1L74 P1L77 P1L91 P1L96..(n=426) peg.no ec.length  
syll.length
1        2      1      0      0      0                86    
5.445         2.479
1        3      1      0      0      0                91    
5.215         2.356
1        4      0      0      0      0                  79    
5.682         2.896
1        5      1      0      0      0               83    
5.149         2.641
1        6      0      0      0      0                77    
5.044         2.288
1        7      1      0      0      0                  78    
5.450         2.420
1        8      1      0      1      1                82    
5.377         2.505
1        9      1      0      0      0                95    
5.389         2.706
1       10      1      0      0      0                88    
5.354         2.461
1       11      1      0      0      0                87    
5.262         3.079   
1       12      1      0      0      0                84    
5.191         2.858
1       13      1      0      0      1                  87    
5.194         2.264
2       23      1      0      0      1                  116    
5.863         2.876
2       24      1      0      0      0                122    
5.475         3.114
2       25      1      0      0      0                110    
5.563         3.059
.       .       .   .     .     .               .         .          .
.       .       .   .     .     .               .         .          .
.
.
(60 families)

'Family' is the first Random effect (categorical variable), with 60 
levels.

All columns labeled P1L(x) are a matrix of presence/absence genetic 
markers for each individual in each family. There are 426 such 
columns(not numbered in sequence) and each one is a random effect.

The last three columns (peg.no, ec.length and syll.length) are the 
three dependent variables.

Each genetic marker column needs to be nested within each family, 
which means that if I take the first phenotype, peg.no, for example, 
then I need to run an analysis that partitions variance 60*426 times 
(~25,00 runs) for that phenotype.
I also need an output with the Anova table, and summary, at each stage 
of the run, so that I can get P values for each marker for each 
family, as a way of determining whether it contributes significantly 
to explaining within-family variance for that trait.

To do the above, I tried to write code for lmer() using two nested 
'for' loops, one for each level of random factor nesting (marker 
within family) as follows (using a test data set [please find 
attached] with only the first 10 marker columns, to see if this works):

vc<-read.table("P:\\R\\Testvcomp10.txt",header=T)
attach(vc)

family<-factor(family)
colms<-(vc)[,4:13] ## this to assign the 10 columns containing marker 

data    to a new variable, as column names are themselves not in any 
recognisable sequence

vcdf<-data.frame(family,peg.no,ec.length,syll.length,colms)
library(lme4)

for (c in levels(family))

+ {    for (i in 1:length(colms))
+        { fit<-lmer(peg.no~1 + (1|c/i), vcdf)
+        }
+    summ<-summary(fit)
+    av<-anova(fit)
+    print(summ)
+    print(av)
+ }

This gives me:

Error in model.frame.default(data = vcdf, formula = peg.no ~ 1 + (1 +  :
 variable lengths differ (found for 'c')



On suggestion from a colleague I reframed this as:

for(c in levels(family))

+ {
+ print("----New C:----")
+ print(c)
+ for (i in 1:length(colms))
+ {
+ fit<-lmer(peg.no~1+ (1|c/i),vcdf)
+ print(i)
+ summ<-summary(fit)
+ av<-anova(fit)
+ print(summ)
+ print(av)
+ }
+ }

..and this gave me the output:

[1] "----New C:----"
[1] "1"
Error in model.frame.default(data = vcdf, formula = peg.no ~ 1 + (1 +  :
 variable lengths differ (found for 'c')


Google-ing the error message has led me to plenty of links that 
suggest forcing data into a data frame to fix this, but that hasn't 
worked.
My markers and phenotypes both have plenty of missing data (NA's in 
the data.frame), and na.action=na.omit isn't solving the problem.

(I have tried this with lme, and tried to do it with the aov() command 
as well and the error is pretty much the same).

I am completely new to R, and despite searching and trying various 
things, can't get the code to work.

I really appreciate any corrections to this code, or alternative 
command/function suggestions that I can look into, to try to do this 
again.


Thanks a bunch for your help,

Aditi


----------------------
A Singh
Aditi.Singh at bristol.ac.uk
School of Biological Sciences
University of Bristol



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