search for: nsnp

I have the following code: ----------------------------------------------------------------------------------------------------- result <- matrix(NA, nrow=1, ncol=5) for(i in 1:(nsnp-1)) { for(j in (i+1):nsnp){ tempsnp1 <- data.lme[,i] tempsnp2 <- data.lme[,j] fm1 <- lme(trait~sex+age+rmtemp.b+fc+tempsnp1+tempsnp2+tempsnp1*tempsnp2, random=~1|famid, na.action=na.omit) fm2 <- lme(trait~sex+age+rmtemp.b+fc+tempsnp1+tempsnp2, random=~1|famid, na.action=na.omit) resul...

...t') # line 2 load('imge.RData') # line3 imge <- imge[,-c(3)] # line4 imge <- imge[complete.cases(imge),] # line5 trait<- read.csv(trait.file) # line6 ngenes <- nrow(imge) # line7 nsnp <- nrow(trait) #line 8 for(i in 1:ngenes) { for(j in 1:nsnp) { if(imge$d1[i]==trait$d1[j] & imge$d2[i]==trait$d2[j]) trait$imgene2[j] <- imge$Gene[i] else trait$imgene2[j] <- NA } } return(trait) } hypertension <- funcname(&q...

...weighted cox ph model, with possible haplotype configurations for each subject weighted by their posterior probabilities given genotype data. Are your markers SNPs? If so you can use a utility function from the hapassoc package to get started. For example, if your data is in a dataframe dat, with nsnp SNPs in the last nsnps columns, you could create an augmented data frame (augmented by pseudo-individuals for each subject with ambiguous phase) with library(hapassoc) ph<-pre.hapassoc(dat,nsnps) augdat<-cbind(ph$nonHaploDM,ph$haploDM) wts<-ph$wt and then use coxph with augdat as the dat...