doc/example_sim_LRGC.R

In udellgroup/mixedgcImp: Missing value imputation for mixed data through Gaussian copula

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ----generate data------------------------------------------------------------
  p = 200
  n = 500
  rank = 10
  sigma = 0.1
  set.seed(410)
  W = matrix(rnorm(p*rank), nrow = p)
  W = t(apply(W, 1, function(x){x/sqrt(sum(x^2))  * sqrt((1-sigma))}))
  Z = matrix(rnorm(n*rank), ncol = rank) %*% t(W) + matrix(rnorm(n*p, sd = sqrt(sigma)), ncol = p)
  # cubic transformation
  X = Z^3
  # mask 40% of the original observation
  X_obs = X
  loc = sample(1:prod(n*p), size = floor(prod(n*p)*0.4))
  X_obs[loc] = NA

## ----fitting------------------------------------------------------------------
library(gcimputeR)
est = impute_mixedgc_ppca(X_obs, rank = 10) # around 8 secs
print("Normalized root mean squared error (NRMSE) is: ")
print(sqrt(mean((est$Ximp[loc] - X[loc])^2))/sqrt(mean(X[loc]^2)))

## ----confidence interval------------------------------------------------------
ct = ct_impute(X_obs, est, 0.95)
print("The empirical coverage is: ")
print(mean(X[loc] >= ct$lower[loc] & X[loc] <= ct$upper[loc]))
print("The mean confidence interval length is: ")
mean(ct$upper[loc] - ct$lower[loc])