On Feb 12, 2010, at 6:48 PM, Nora Kozloff wrote:
This will probably seem very simple to experienced R programmers:
I am doing a snp association analysis and am at the model-fitting
stage. I
am using the Stats package's "drop1" with the following code:
##geno is the dataset
## the dependent variable (casectrln) is dichotomous and coded 0,1
## rs743572_2 is one of the snps (which is coded 0,1,2 for the 3
genotypes)
library(stats)
modadd = glm(geno$casectrln ~rs743572_2 + factor(racegrp)+
factor(smokgp)+
factor(alcgp)+ factor(bmigp) + factor(ipssgp)
+ agebase, family="binomial")
drop1(mod,scale=0,test=c("Chisq"), x=NULL, k=2)
There is a great deal of missing data in this dataset for both snps
and for
covariates--so I have been instructed not to simply drop all cases
with
missing genotype or covariate data .
It sounds as though you have been asked to do some sort of imputation
of missing data.
How can I drop the observations which
are missing only for the snp I am modeling at the time, then
reinstate
those observations to model the next snp?
Thanks for any help,
Nora
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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