Wouldn't the best approach be to adapt the acf function already
available in R and change it only to suit your needs?
cheers,
Paul
baloo mia wrote:
In the ACF(nlme) the normalization of the numerator has been done by N and I
want to normalize it by N-k, where N is the observations and k is the lag.
Baloo
--- On Tue, 12/22/09, ONKELINX, Thierry <thierry.onkel...@inbo.be> wrote:
From: ONKELINX, Thierry <thierry.onkel...@inbo.be>
Subject: RE: [R] Nested For loops
To: "baloo mia" <baloo_...@yahoo.com>, r-help@r-project.org
Date: Tuesday, December 22, 2009, 1:00 AM
Baloo,
Why don't you use the built-in acf function?
Thierry
----------------------------------------------------------------------------
ir. Thierry Onkelinx
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Belgium
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team Biometrics & Quality Assurance
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tel. + 32 54/436 185
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-----Oorspronkelijk bericht-----
Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Namens
baloo mia
Verzonden: dinsdag 22 december 2009 2:07
Aan: r-help@r-project.org
Onderwerp: [R] Nested For loops
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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