Nothing
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))
})
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