tests/testthat/test-corrData_fill.R

In miceFast: Fast Imputations Using 'Rcpp' and 'Armadillo'

context("corrData-fill")


test_that("fill-data", {
  set.seed(1234)

  # parameters

  power <- 4 # power of 10 - number of observations - should be adjusted to a computer capabilities

  nr_var <- 7 # CHANGE - only if you generate a bigger corr matrix:  number of variables - independent and one dependent

  grs <- max(c(10**(power - 3), 10)) # grouping variable - number of groups

  iters <- 10 # number of iterations for benchmarking

  ## generete example - data

  ## positive-defined correlation matrix

  cors <- matrix(c(
    1, 0.6, 0.7, 0.4, 0.4, 0.5, 0.35,
    NA, 1, 0.2, 0.05, 0.1, 0.12, 0.15,
    NA, NA, 1, 0.15, 0.15, 0.1, 0.08,
    NA, NA, NA, 1, 0.12, 0.15, 0.1,
    NA, NA, NA, NA, 1, 0.15, 0.2,
    NA, NA, NA, NA, NA, 1, 0.15,
    NA, NA, NA, NA, NA, NA, 1
  ), 7, 7, byrow = T)

  cors[lower.tri(cors)] <- t(cors)[lower.tri(cors)]

  # automatic corr matrix - close to diagonal

  # cors = stats::rWishart(100,nr_var+10,diag(nr_var))

  # cors = apply(cors,1:2,mean)/(nr_var+10)

  # cors

  ##

  model <- new(corrData, 10, 10^power, rep(0, nr_var), cors)

  data_bin <- model$fill("binom")
  data_disc <- model$fill("discrete")
  data_con <- model$fill("contin")

  rm(model)

  cor_b <- cor(data_bin)
  cor_d <- cor(data_disc)
  cor_c <- cor(data_con)

  cor_cor_a <- cor(cbind(as.vector(cors), as.vector(cor_c)))[1, 2] > 0.99
  cor_cor_b <- cor(cbind(as.vector(cors), as.vector(cor_c)))[1, 2] > 0.99
  cor_cor_c <- cor(cbind(as.vector(cors), as.vector(cor_c)))[1, 2] > 0.99

  expect_true(all(cor_cor_a, cor_cor_b, cor_cor_c))
})