HI R-experts,
I am trying to speed up my calculation of the A results below and replace the
for loop withsome functionals like lapply. After manyreadings, trial and
error, I still have no success. Would anyone please give me some hints onthat?
Thank you in advance.
Anne
The program is this, I have a complicated function and itneeds to operate on
some subsets of a dataset many times, depending on thevalues of group. I
simplify the functionand dataset for this example run.
getResult <- function(d) {
#examplefunction
weighted.mean(x=d[,1], w=d[,2])
}
#example data setup
n=20;
set.seed(1)
g=rep(1:5,each=4)
df=as.data.frame(cbind( sort(rnorm(mean=15,sd=10, n)),runif(n), rbinom(n, 1,
0.4) , g )); df
getResult(df)
i0=c(1,2,4,5,5)
ng= length(unique(g))
#initiation of result matrix
A=matrix(Inf, ng, ng); A
for(i in 1:ng)
{ cat("i:",i,"")
for(jin i0[i]:ng) {
ok= !is.na(match(g,i:j)); cat("j:",j,"\n");
A[i,j]=getResult(d=df[ok,])
} #endfor (j)
} #end for (i)
Is there an elegant way to remove the for loop here? I try to make it flat for
faster run but Icannot figure out how to subset the observations faster without
error to apply the functiongetResult. Any hint is appreciated.
on another note, is there a more elegant way to initiate the list as follows?
mylist=list(); w=rep(4,5)
for (i in 1:5) mylist[[i]]=w[i:5]
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