View source: R/cov-miss-mix-wt.R
cov.miss.mix.wt | R Documentation |
The weighted means and variances using the observation matrix and the estimated weight vectors for a data matrix containing missing values (NA or NaN)
cov.miss.mix.wt( x, means, secm, wt1 = rep(1/nrow(x), nrow(x)), wt2 = rep(1/nrow(x), nrow(x)), cor = FALSE, center = TRUE, method = c("unbiased", "ML") )
x |
the observation matrix, which can contain missing values (NA or NaN) |
means |
a list containing the means of the missing values given observed values |
secm |
a list containing the second moments of the missing values given observed values |
wt1 |
the state probabilities matrix (number of observations times number of states) |
wt2 |
the mixture components probabilities list (of length nstate) of matrices (number of observations times number of mixture components) |
cor |
logical. if TRUE the weighted correlation is also given |
center |
logical. if TRUE the weighted mean is also given |
method |
with two possible entries:
|
list containing the following items:
center
the weighted mean of x
cov
the weighted covariance of x
n.obs
the number of observations in x
cor
the weighted correlation of x
,
if the parameter cor
is TRUE
wt1
the state weighs wt1
wt2
the mixture component weights wt2
pmix
the estimated mixture proportions
Morteza Amini, morteza.amini@ut.ac.ir
data(CMAPSS) x0 = CMAPSS$train$x[1:CMAPSS$train$N[1], ] n = nrow(x0) wt1 = runif(n) wt2 = runif(n) p = ncol(x0) sammissall = sample(1:n, trunc(n / 20)) means = secm = list() for(ii in 1:n){ if(ii %in% sammissall){ means[[ii]] = colMeans(x0[sammissall, ]) secm[[ii]] = t(x0[sammissall, ]) %*% x0[sammissall, ] }else{ means[[ii]] = secm[[ii]] = NA } } x0[sammissall,] = NA cov.miss.mix.wt(x0, means, secm, wt1, wt2)
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