Hi all

 

I have been doing series of linear regression models lm(). In this case the execution time and memory usage becomes a huge issue. I have therefore been trying to speed the process and limit the memory usage. 

 

Here follows part of the code do give better understanding of what I am doing:

 

modela <- lm(RSSYS10 ~ RS_AGE + RS_AGESQ + SEX + RS_BMI)

frssys <- function(SNP_A1,data=sys_pheno, model=modela){

            modelb <- lm(RSSYS10 ~ RS_AGE + RS_AGESQ + SEX + RS_BMI + SNP_A1,data=data)

modelc <- lm(RSSYS10 ~ RS_AGESQ + SEX + RS_BMI + SNP_A1 * RS_AGE,data=data)

p1 <- anova(model,modelb)$P[2] 

p2 <- anova(model,modelc)$P[2] 

p3 <- anova(modelb,modelc)$P[2]           

}

result_matrix <- apply(mldose_asmatrix[,2:ncol(mldose_asmatrix)],2,frssys)

 

Data frame sys_pheno has all variables except SNP_A1. In matrix mldose_asmatrix is a series of variables (columns), which are called SNP_A1 in the function frssys. Matrix mldose_asmatrix is very big with up to 50 000 columns and it happens that R runs out of memory. 

 

Does anyone have an idea how I can save memory and/or speed up the process?

 

Regards,

Gauti Gislason

 








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