[R] Nested For loops

[Paul Hiemstra]

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.ONKELINX at inbo.be> wrote:
>
> From: ONKELINX, Thierry <Thierry.ONKELINX at inbo.be>
> Subject: RE: [R] Nested For loops
> To: "baloo mia" <baloo_mia at yahoo.com>, r-help at 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
> Instituut voor natuur- en bosonderzoek
> team Biometrie & Kwaliteitszorg
> Gaverstraat 4
> 9500 Geraardsbergen
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>
> Research Institute for Nature and Forest
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>
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> ~ Sir Ronald Aylmer Fisher
>
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> ~ Roger Brinner
>
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>  
> -----Oorspronkelijk bericht-----
> Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] Namens baloo mia
> Verzonden: dinsdag 22 december 2009 2:07
> Aan: r-help at 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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-- 
Drs. Paul Hiemstra
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