[R] efficient code. how to reduce running time?
John Fox
jfox at mcmaster.ca
Sun Jan 21 23:00:44 CET 2007
Dear Mira,
I didn't work through your code in detail, but I did notice that you're
doing something that's very inefficient in R -- building up objects
element-by-element, e.g., by indexing beyond their current length. Instead,
you can preallocate the objects and simply replace elements. For example,
create the vector F in your perm.F() as F <- numeric(nperms) rather than as
an empty vector. (BTW, I'd probably not name a variable "F" since this is
usually a synonym for the logical value FALSE.) There's a similar problem in
your handling of maxF, which you build up column-by-column via cbind().
(BTW, is maxF really a matrix?) You also needlessly recompute max(F), never
appear to use MSSid, and end lines with unnecessary semicolons.
I hope that this helps,
John
--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox
--------------------------------
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of miraceti
> Sent: Sunday, January 21, 2007 12:38 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] efficient code. how to reduce running time?
>
> Hi,
> I am new to R.
> and even though I've made my code to run and do what it needs to .
> It is taking forever and I can't use it like this.
> I was wondering if you could help me find ways to fix the
> code to run faster.
> Here are my codes..
> the data set is a bunch of 0s and 1s in a data.frame.
> What I am doing is this.
> I pick a column and make up a new column Y with values
> associated with that column I picked.
> then I remove the column.
> and see if any other columns in the data have a significant
> association with the Y column I've generated.
> If you see anything obvious that takes a long time, any
> suggestions would be appreciated.
> thanks a lot.
>
> Mira
>
> --------------------------------------------------------------
> --------------------------------
> #sub function for finding index
> rfind <- function(x)seq(along=x)[x*(1-x)>MAF*(1-MAF)]
>
> #sub function for permutation test
> perm.F = function(y,x,nperms,sites)
> {
> maxF = c();
> for (i in 1:nperms)
> {
> F=numeric(S) #create an empty vector to store the F-values
> newY=sample(y,length(y)) #permute the cancer types
> newX = cbind(x, newY);
> # anova for all sites
> for ( i in sites )
> {
> a <- anova(lm(newY~factor(newX[,i])));
> F[i] <- a$`F value`[1];
> }
> MSSid <- which (F == max(F)); # index of MSS (Most
> Significant Site)
> maxF = cbind(maxF,max(F));
> }
> maxF;
> }
>
>
> # set the output file
> sink("/tmp/R.out.3932.100")
> # load the dataset
> snp = read.table(file("/tmp/msoutput.3932.100"))
> #print (snp);
>
> # pi: desired proportion of variation due to QTN pi = 0.05;
> print (paste("pi:", pi)); MAF = 0.05; print (paste("MAF:",
> MAF)); # S: number of segregating sites S = length(snp[1,]);
> # N: number of samples N = length(snp[,1]); Dips =
> sample(1:N,N) DipA = Dips[1:50] DipB = Dips[51:100] disnp =
> snp[DipA,]+snp[DipB,] snp = as.data.frame(disnp,
> row.names=NULL); N = length(snp[,1]);
>
> # get allele freq for all SNPs
> allele_f <- mean(snp[,1:S])/2;
> print (allele_f);
> sites = rfind(allele_f);
> print(sites);
>
> # collapse sites that have perfect correlation newsites <-
> sites; for (i in 1:(length(sites)-1)) {
> for (j in (i+1):length(sites))
> {
> test = (snp[sites[i]] == snp[sites[j]])
> if ( all(test) || all(!test) )
> {
> print (paste("perfect correlation with", sites[i]));
> print (paste("removing alleles", sites[j]));
> newsites <- newsites[newsites!=sites[j]];
> }
> }
> }
> sites <- newsites;
> print(sites);
>
> # QTN: the site nearest right to S/4
> sitesid = floor(length(sites)/4);
> QTNid = sites[sitesid];
> QTN = snp[,QTNid];
>
> print (paste("QTN:", names(snp[QTNid]))); print (QTN);
>
> # remove QTN from sites
> sites <- sites [ sites != QTNid ];
> print(sites);
> print (paste("Number of usable SNPs:", length(sites)));
>
> # p: allele frequency of QTN
> p0 = allele_f[QTNid];
> p = min(p0, 1-p0);
> print (paste("QTN_freq:", p));
>
> # z: random normal deviate
> z = rnorm(N, mean = 0, sd = 1);
> # foreach sample give quantitative phenotype # each row is a
> sample # phenotype value depends on QTN genotype, pi, p, and
> z Y <- sqrt(10-(10*pi))*z + QTN*sqrt((10*pi)/(2*p*(1-p)));
> snp = data.frame(cbind(snp, Y)); # anova for QTN
> df=data.frame(Y=Y, QTN=factor(QTN)); QTN_a <- anova(lm(Y~QTN,
> data=df)); print (QTN_a); SSB <- QTN_a$`Sum Sq`[1]; SSW <-
> QTN_a$`Sum Sq`[2]; QTN_PRE <- SSB / (SSB + SSW); print
> (paste("var_QTN/var_tot:", QTN_PRE));
>
> # anova for all sites
> F=numeric(S) #create an empty vector to store the F-values
> Pval=rep(1,S) #create an empty vector to store the Pval
> PRE=numeric(S) #create an empty vector to store the PRE
>
> for ( i in sites )
> {
> a <- anova(lm(Y~factor(snp[,i])));
> print (a);
> F[i] <- a$`F value`[1];
> Pval[i] <- a$`Pr`[1];
> SSB <- a$`Sum Sq`[1];
> SSW <- a$`Sum Sq`[2];
> PRE[i] <- SSB / (SSB + SSW);
>
> }
> print (paste("Max F:", max(F)));
> MSSid <- which (F == max(F)); # index of MSS (Most
> Significant Site) MSS = snp[,MSSid]; print (paste("MSS(Most
> Significant Site):", MSSid)); p0 = length(MSS[MSS==0])/N; p =
> min(p0, 1-p0); print (paste("assoc_freq:", p)); print
> (paste("assoc_var:", PRE[MSSid])); #lets do a permutation
> test Fdist <- perm.F(Y, snp[,1:S], 1000, sites); print
> ("permutation test maxF dist"); print (Fdist); pvalue <-
> mean(Fdist>F[MSSid]); print (paste("assoc_prob:", pvalue));
>
> # close the output file
> sink()
> --------------------------------------------------------------
> ------------------------------------
>
> [[alternative HTML version deleted]]
>
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