r - Principal Component Rotation matrix with different dimension -


i not able understand why happening. have data matrix (64x6830). when following

pr.out=prcomp(data,scale=true) dim(pr.out$rotation) # [1] 6830   64

i not able understand why rotation matrix not 6830x6830. when take subset of data this: 

data1=data[1:nrow(data),1:10] pr.data=prcomp(data1,scale=true) dim(pr.data$rotation) # [1] 10 10

so smaller size same data giving correct, clueless why giving different rotation matrix when done on whole dataset.

the function prcomp based on function svd:

svd(x, nu = min(n, p), nv = min(n, p), linpack = false)

from edit(stats:::prcomp.default), see:

s <- svd(x, nu = 0)

this means left singular vectors not computed. thus, in case of prcomp, svd returns "a vector containing singular values of x, of length min(n, p)" , "a matrix columns contain right singular vectors of x [...]. dimension c(p, nv)"

if go call of svd, nv defined nv = min(n, p) (minimum between n , p), n = row(x) , p = ncol(x).

  1. in case of "data", n = 64 , p = 6830. nv = 64 , pr.out$rotation 6830x64 (p x nv) matrix
  2. in case of "data1", n = 10 , p = 10. nv = 10 , and pr.out$rotation 10x10 (p x nv) matrix

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