Weighted Expectations and Variances
wt.var estimate the unbiased variance taking into account
data weights.
wt.moments produces the weighted mean and weighted variance
for each column of a matrix.
wt.scale centers and standardized a matrix using
the weighted means and variances.
wt.var(xvec, w) wt.moments(x, w) wt.scale(x, w, center=TRUE, scale=TRUE)
xvec |
a vector |
x |
a matrix |
w |
data weights |
center |
logical value |
scale |
logical value |
A rescaled matrix (wt.scale), a list containing the column means and
variances (wt.moments), or single number (wt.var)
Korbinian Strimmer (https://strimmerlab.github.io).
# load corpcor library
library("corpcor")
# generate some data
p = 5
n = 5
X = matrix(rnorm(n*p), nrow = n, ncol = p)
w = c(1,1,1,3,3)/9
# standardize matrix
scale(X)
wt.scale(X)
wt.scale(X, w) # take into account data weightsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.