Dear R experts,
Might be very simple question to ask but would be insightful. As the same story
of nested "for loops". following is the code that I am using to get the
autocorrelation function of the sample data. I have tried to get rid of for
loops but since I am touching R after such a long time that I need to practice
more but I need help to revive my skills. I know that apply() or sapply() would
be beneficial. I need help...
Best Wishes,
Baloo
R code
----------
acf_wal <- function (infile) {
data<-read.table(infile)
data_value <- data[,-1]
data_value_mean <- mean(data_value)
data_value_square <- (data_value - data_value_mean) ^ 2
square_sum<-sum(data_value_square)
entry<-NROW(data_value)
deno<-square_sum/entry
tab1<-c()
tab2<-c()
ps_value <- seq(0,(floor(entry/2)),1)
for(k in 0:(floor(entry/2))){
for (i in 1:(entry-k)) {
mult<-(data_value[i] - data_value_mean) * (data_value [i+k] -
data_value_mean)
tab1 <- c(tab1,mult)
}
auto_avg<-mean(tab1)
tab1<-c()
auto_corr<-auto_avg/deno
tab2<-c(tab2,auto_corr)
}
table_value <- cbind (ps_value, tab2)
colnames(table_value) <- c("#ps", "acf")
outfile<-unlist(strsplit(infile, split=".", fixed=TRUE))[1]
write.table(table_value,file=paste(outfile,"acf.dat",sep="-"),row.names=FALSE,sep="\t",quote=F)
}
--------------
Sample data
------------------
1 16.0071
2 16.7966
3 17.575
4 18.1614
5 15.982
6 16.8515
7 15.6828
8 14.9652
9 14.8623
10 14.7079
--------------------
Help in this regard would be highly appreciated.
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