Nothing
## Basic tests for impute_LS_array() -------------------------------------------
test_that("impute_LS_array() works (basic test, only check for anyNA)", {
set.seed(1234)
ds_mis <- mvtnorm::rmvnorm(20, rep(0, 5), diag(1, 5))
ds_mis <- delete_MCAR(ds_mis, 0.2, 1:4)
expect_false(anyNA(impute_LS_array(ds_mis)))
})
test_that("impute_LS_array() works with completely missing row and verbose", {
set.seed(1234)
ds_mis <- mvtnorm::rmvnorm(20, rep(0, 5), diag(1, 5))
ds_mis[5, ] <- NA
# silent
ds_imp_silent <- expect_silent(
impute_LS_array(ds_mis, verbose_gene = FALSE, verbose_expected_values = FALSE)
)
expect_false(anyNA(ds_imp_silent))
expect_equal(ds_imp_silent[5, ], suppressWarnings(colMeans(impute_LS_gene(ds_mis))))
# verbose_gene
ds_imp_verb1 <- expect_message(
impute_LS_array(ds_mis, verbose_gene = TRUE, verbose_expected_values = FALSE),
"No observed value in row(s) 5. These rows were imputed with column means.",
fixed = TRUE,
all = TRUE
)
expect_equal(ds_imp_verb1, ds_imp_silent)
# verbose_expected_values
ds_imp_verb2 <- expect_message(
impute_LS_array(ds_mis, verbose_gene = FALSE, verbose_expected_values = TRUE),
"The missing values of following rows were imputed with (parts of) mu: 5",
fixed = TRUE,
all = TRUE
)
expect_equal(ds_imp_verb2, ds_imp_silent)
# verbose_gene and verbose_expected_values
verify_output(
test_path("test-impute_LS_array-verbosity.txt"),
ds_imp <- impute_LS_array(ds_mis, verbose_gene = TRUE, verbose_expected_values = TRUE)
)
ds_imp_verb3 <- suppressWarnings(impute_LS_array(ds_mis, verbose_gene = TRUE, verbose_expected_values = TRUE))
expect_equal(ds_imp_verb3, ds_imp_silent)
})
test_that("impute_LS_array() works with small data frames", {
expect_equal(
impute_LS_array(data.frame(X = 1:11, Y = c(1:10, NA))),
data.frame(X = 1:11, Y = 1:11)
)
})
## Comparing impute_LS_array() to original results from Bo et al. -------------
# For some remarks see test-impute_LS_gene.R
test_that("impute_LS_array() works with dataset triangle miss", {
# The missing values in this file were created with upper.tri, which results in a monotone pattern.
# The rows 1:15 have 1:15 observed values.
ds_triangle_mis <- readRDS(test_path(file.path("datasets", "ds_triangle_mis.rds")))
ds_triangle_LS_array_Bo <- readRDS(test_path(file.path("datasets", "ds_triangle_LS_array_Bo.rds")))
expect_equal(
ds_triangle_LS_array_Bo,
round(impute_LS_array(ds_triangle_mis, min_common_obs = 5), 3)
)
})
test_that("impute_LS_array() imputes like Bo et al. (2004) (MCAR, 100x7)", {
# Use LS_gene imputed dataset from Bo et al. (2004) as base for LS_array
ds_100x7_LS_gene_Bo <- readRDS(test_path(file.path("datasets", "ds_100x7_LS_gene_Bo.rds")))
ds_100x7_mis_MCAR <- readRDS(test_path(file.path("datasets", "ds_100x7_mis_MCAR.rds")))
# Cure some rounding problems due to saving:
ds_mis <- ds_100x7_LS_gene_Bo
ds_mis[is.na(ds_100x7_mis_MCAR)] <- NA
ds_imp <- round(impute_LS_array(ds_mis, min_common_obs = 5, ds_impute_LS_gene = ds_100x7_LS_gene_Bo), 3)
ds_100x7_LS_array_Bo <- readRDS(test_path(file.path("datasets", "ds_100x7_LS_array_Bo.rds")))
# Need some tolerance because of rounding:
expect_equal(ds_100x7_LS_array_Bo, ds_imp, tolerance = 0.005)
# All differences are smaller than 0.002: (round 3 digits!)
expect_equal(sum(abs(ds_100x7_LS_array_Bo - ds_imp) >= 0.002), 0)
# Conclusion: Both methods seem to return the same imputation values (only deviations because of rounding)
})
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