I'm trying to speed up a script that otherwise takes days to handle larger
data sets. So, is there a way to completely vectorize or paralellize the
following script:
*# k-fold cross validation*
df <- trees # a data frame 'trees' from R.
df <- df[sample(nrow(df)), ] # randomly shuffles the data.
k <- 10 # Number of folds. Note k=nrow(df) in the leave-one-out cross
validation.
folds <- cut(seq(from=1, to=nrow(df)), breaks=k, labels=FALSE) # creates
unique numbers for k equally size folds.
df$ID <- folds # adds fold IDs.
df[paste("pred", 1:3, sep="")] <- NA # adds multiple columns "pred1"
"pred2" "pred3" to speed up the following loop.
library(mgcv)
for(i in 1:k) {
# looping for different models:
m1 <- gam(Volume ~ s(Height), data=df, subset=(ID != i))
m2 <- gam(Volume ~ s(Girth), data=df, subset=(ID != i))
m3 <- gam(Volume ~ s(Girth) + s(Height), data=df, subset=(ID != i))
# looping for predictions:
df[df$ID==i, "pred1"] <- predict(m1, df[df$ID==i, ], type="response")
df[df$ID==i, "pred2"] <- predict(m2, df[df$ID==i, ], type="response")
df[df$ID==i, "pred3"] <- predict(m3, df[df$ID==i, ], type="response")
}
# calculating residuals:
df$res1 <- with(df, Volume - pred1)
df$res2 <- with(df, Volume - pred2)
df$res3 <- with(df, Volume - pred3)
Model <- paste("m", 1:3, sep="") # creates a vector of model names.
# creating a vector of mean-square errors (MSE):
MSE <- with(df, c(
sum(res1^2) / nrow(df),
sum(res2^2) / nrow(df),
sum(res3^2) / nrow(df)
))
model.mse <- data.frame(Model, MSE) # creates a data frame of model names
and mean-square errors.
model.mse <- model.mse[order(model.mse$MSE), ] # rearranges the previous
data frame in order of increasing mean-square errors.
I'd appreciate any help. This code takes several days if run on >=30,000
different GAM models and 3 predictors. Could you please help with
re-writing the script into sapply() or foreach()/doParallel format?
Thanks
Lexo
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