Hi, yes a loop would work. Maybe there are other options also. Bootstrapping
a PCA with a loop can be done as follows. You will have to adjust the
dimension of the matrices/array according to the dimensions of your data and
according to the values that you want to retrieve from your PCA.
####Start of example
##Iris data
data=iris
##Inspect data
data
##Perform pca on columns 1:4
x=prcomp(data[,1:4])
##Inspect results of pca
summary(x)
summary(x)$importance
trials=10 ##we want 10 bootstraps
##We want to bootstrap pca
##and, say, know some information
##contained in "summary(x)$importance"
##Thus, we create an array that has the
##dimension of "summary(x)$importance"
##and depth of "trials"
importance=array(0,c(3,4,trials))
##Bootstrap the PCA
for(i in 1:trials){
##sample IDs of original data rows
sam=sample(1:length(data[,1]),replace=T)
##sample new data from old data
##using the data row IDs from above
newdata=data[c(sam),]
##store the results of the
##importance matrix resulting from PCA
##performed on columns 1:4
##of our bootstrapped data
importance[,,i]=summary(prcomp(newdata[,1:4]))$importance
}
##Now we can assess, for example,
##the mean and SD
##of the accounted variance of the
##first principal component
##mean of the variance accounted for
##by the first PC over all trials
mean(importance[2,1,])
##sd of the variance accounted for
##by the first PC over all trials
sd(importance[2,1,])
####End of example
Best,
Daniel
-------------------------
cuncta stricte discussurus
-------------------------
-----Ursprüngliche Nachricht-----
Von: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Im
Auftrag von Tanya Yatsunenko
Gesendet: Monday, June 30, 2008 10:09 PM
An: r-help@r-project.org
Betreff: [R] PCA : Error in eigen(cv,
Hi all,
I am doing bootstrap on a distance matrix, in which samples have been drawn
with replacement. After that I do PCA on a resulted matrix, and these 2
steps are repeated 1000 times.
pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from
1 to 1000
SampleD is a new matrix after resampling;
I am getting the following error message, which I don't understand:
....
+pca(x)<-princomp(SampleD[i,j])
+ }
Error in eigen(cv, symmetric = TRUE) : infinite or missing values in 'x'
Should I maybe not use a vector, but matrix instead?
Thanks!
--
Tanya.
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