pca - Extract Identifier from Principal Component Analysis with Missing Data in R -

i conducting principal component analysis in r on vectors missing data. want extract score principal component , match values observations not missing in original frame can't figure out how extract , match on right identifiers. example:

x1 <- c(1,2,3,na, 5,6,7) x2 <- c(7,na,6,na, 4,3,2)  frame <- cbind(x1,x2)  pca_ob<- princomp(~frame) pca_ob$score[,1] 

this produces following output:

    1         3         5         6         7    4.273146  2.104705 -0.715732 -2.125950 -3.536168  

i bind pca_ob$score[,1] original frame based on identifiers , fill rest in nas such produces following matrix:

    x1 x2 x3 1    1  7  4.273146 2    2  na na 3    3  6  2.104705 4    na na na 5    5  4  -0.715732 6    6  3  -2.125950 7    7  2  -3.536168 

this takes output of first set of scores , matches them frame nas filling spots there isn't pca score , matching on variables there scores.any thoughts? thanks.

this feels bit of hack. there may better solution out there somewhere.

the method here create new object full of nas, , turn names of sparse data numeric indexes , assign using those.

> p1 <- pca_ob$scores[,1] > p1         1         3         5         6         7   4.273146  2.104705 -0.715732 -2.125950 -3.536168  > z<-rep(na, 7) > z[as.numeric(names(p1))]<-p1 > z [1]  4.273146        na  2.104705        na -0.715732 -2.125950 -3.536168