Hi R user,
I was trying to develop a model (logistic regression) for 4001 dependent
variables using 15 environmental variables (45000 rows); and then trying to use
the models to predict in future. I used following code but it took so much time
and consumed 100% of the PC memory. Even though- analysis was not complete. I
got a following message
" Reached total allocation of 8098Mb: see help(memory.size)". I increased
memory size to 8GB. but still I could not complete the analysis.
Any suggestion to reduce the memory and compute the big data set.
#------------------------------------------------------------------
data=spec.Env
models <- list()
PredictModelsCur<-list()
PredictModelsA1<-list()
PredictModelsA2<-list()
PredictModelsA3<-list()
dvnames <- paste("V", 2:4003, sep="")
ivnames <- paste("env", 1:15, sep="",collapse="+") ## for some value of n
for (y in dvnames){
form <- formula(paste(y,"~",ivnames))
models[[y]] <- glm(form, data=spec.Env, family='binomial')
PredictModelsCur[[y]]<-predict(models[[y]], type="response")
PredictModelsA1[[y]]<-predict(models[[y]], data = a1.Futute,
type="response")
PredictModelsA2[[y]]<-predict(models[[y]], data = a2.Futute,
type="response")
PredictModelsA3[[y]]<-predict(models[[y]], data = a3.Futute,
type="response")
}
write.csv(PredictModelsCur, "PredictModelsCur.csv", row.names=F)
write.csv(PredictModelsA1, "PredictModelsA1.csv", row.names=F)
write.csv(PredictModelsA2, "PredictModelsA2.csv", row.names=F)
write.csv(PredictModelsA3, "PredictModelsA3.csv", row.names=F)
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