context("Sample description")
test_that("No error if all data filtered (n.subset is 0)",
{
expect_warning(SampleDescription(n.total = 0, n.subset = 0, n.estimation = 0,
"lab", weighted = FALSE, missing = TRUE, imputation.label = "",
m = 1, ""))
})
test_that("Effective sample size", {
expect_equal(SampleDescription(n.total = 302, n.subset = 200,
n.estimation = 100, "lab",
weighted = TRUE,
weight.label = "wgt",
missing = "Exclude cases with missing data",
effective.sample.size = 127.07),
paste0("n = 100 cases used in estimation of a total sample size of 200 (lab); ",
"data has been weighted (wgt); effective sample size: 127.07; ",
"cases containing missing values have been excluded;"))})
test_that("Missing value strings", {
n.tot <- 123
n.sub <- 123
n.est <- 100
# Missing data excluded
expect_equal(SampleDescription(n.total = n.tot, n.subset = n.sub,
weighted = FALSE, n.estimation = n.est,
missing = "Exclude cases with missing data"),
paste0("n = ", n.est, " cases used in estimation of a total sample size of ", n.sub, "; ",
"cases containing missing values have been excluded;"))
# Error if missing
expect_equal(SampleDescription(n.total = n.tot, n.subset = n.sub,
weighted = FALSE, n.estimation = n.est,
missing = "Error if missing data"),
paste0("n = ", n.est, " cases used in estimation of a total sample size of ", n.sub, ";"))
# Single imputation
expect_equal(SampleDescription(n.total = n.tot, n.subset = n.sub,
weighted = FALSE, n.estimation = n.est,
missing = "Imputation (replace missing values with estimates)",
imputation.label = "chained equations (predictive mean matching)",
variable.description = "predictor"),
paste0("n = ", n.est, " cases used in estimation of a total sample size of ", n.sub, "; ",
"missing values of predictor variables have been imputed using chained equations ",
"(predictive mean matching);"))
# Multiple imputation
expect_equal(SampleDescription(n.total = n.tot, n.subset = n.sub,
weighted = FALSE, n.estimation = n.est,
missing = "Multiple imputation", m = 2,
imputation.label = "chained equations (predictive mean matching)",
variable.description = "predictor"),
paste0("n = ", n.est, " cases used in estimation of a total sample size of ", n.sub, "; ",
"multiple imputation (m = 2, chained equations (predictive mean matching)) has been ",
"used to impute missing values of predictor variables;"))
# Dummy variable adjustment selected but no dummy variables created. No dummy variable message
# should appear in footer
expect_equal(SampleDescription(n.total = n.tot, n.subset = n.sub,
weighted = FALSE, n.estimation = n.est,
missing = "Dummy variable adjustment",
dummy.adjusted = FALSE),
paste0("n = ", n.est, " cases used in estimation of a total sample size of ", n.sub, ";"))
# Expect dummy variable adjustment method message to appear
expect_equal(SampleDescription(n.total = n.tot, n.subset = n.sub,
weighted = FALSE, n.estimation = n.est,
missing = "Dummy variable adjustment",
dummy.adjusted = TRUE),
paste0("n = ", n.est, " cases used in estimation of a total sample size of ", n.sub, "; ",
"missing values of variables have been adjusted using dummy variables;"))
})
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