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tests/testthat/_test-svyCreateTableOne.R
In tableone: Create 'Table 1' to Describe Baseline Characteristics with or without Propensity Score Weights
################################################################################
### Unit tests for the svyCreateTableOne
## Reference: http://adv-r.had.co.nz/Testing.html
## Created on: 2015-07-24
## Author: Kazuki Yoshida
################################################################################
###
### Structure
## expectations within tests within context
###
### Prepare environment
################################################################################
library(testthat)
library(survey)
###
### Use oldsvyquantile when available
################################################################################
## 2020-07-30 via Thomas Lumley
## survey::svyquantile rewritten for survey 4.1
## To get the old behaviour you can simply call survey::oldsvyquantile() instead.
try_oldsvyquantile <- try(getFromNamespace(x = "oldsvyquantile", ns = "survey"))
if (is.function(try_oldsvyquantile)) {
svyquantile <- survey::oldsvyquantile
}
###
### Context (1 for each file)
################################################################################
context("Unit tests for svy* user functions")
###
### Provide data
################################################################################
## Three-group dataset with matching weight (Li & Greene 2013)
datMw <- structure(list(E = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), C = c(1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L), Y = c(0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1), C1 = c(0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1
), C2 = c(0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,
0, 1, 1, 0, 0, 1, 1), n = c(252L, 28L, 105L, 45L, 12L, 28L, 1L,
9L, 72L, 8L, 63L, 27L, 24L, 56L, 1L, 9L, 36L, 4L, 42L, 18L, 24L,
56L, 8L, 72L), Mw = c(0.1429, 0.1429, 0.4, 0.4, 1, 1, 1, 1, 0.5,
0.5, 0.6667, 0.6667, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.125,
0.125)), class = "data.frame", .Names = c("E", "C", "Y", "C1",
"C2", "n", "Mw"), row.names = c(NA, -24L))
## Create individual level data
datMw <- datMw[rep(seq_along(datMw$n), datMw$n),]
## Introduce missingness
datMw[c(1,150), "Y"] <- NA
## Create a survey design object
datSvy <- svydesign(ids = ~ 1, data = datMw, weights = ~ Mw)
## Create a variable list
vars <- c("E", "C", "Y", "C1", "C2")
factorVars <- c("Y","C1")
###
### Tests
################################################################################
## A test should group together expectations for one functionality.
### Error handling
test_that("data assessment detects anomalies", {
## Error in ModuleStopIfNoVarsLeft(vars) (from modules.R#52) : No valid variables.
## In addition: Warning message:
## In ModuleReturnVarsExist(vars, data$variables) :
## The data frame does not have: none Dropped
expect_warning(expect_error(svyCreateTableOne(vars = "non-existent", data = datSvy),
"No valid variables"),
"The data frame does not have: non-existent Dropped")
## Error in StopIfNotSurveyDesign(data) :
## The data argument needs to be a survey design object.
expect_error(svyCreateTableOne(vars = c("E"), data = "not a svy object"),
"The data argument needs to be a survey design object")
## Data frame is not allowed
expect_error(svyCreateTableOne(vars = c("E"), data = data.frame(E = c(1,2,3))),
"The data argument needs to be a survey design object")
## Error in ModuleReturnStrata(strata, data$variable) (from modules.R#84) :
## None of the stratifying variables are present in the data frame.
## In addition: Warning message:
## In ModuleReturnVarsExist(strata, data) :
## The data frame does not have: non-existent Dropped
expect_warning(expect_error(svyCreateTableOne(vars = vars, strata = c("non-existent"), data = datSvy),
"None of the stratifying variables are present in the data frame"),
"The data frame does not have: non-existent Dropped")
})
### User-level functionalities
## Make categorical variables factors
## Create a table to test
mwOverall <- svyCreateTableOne(vars = vars, data = datSvy, factorVars = factorVars)
mwInclNa <- svyCreateTableOne(vars = vars, data = datSvy, factorVars = factorVars, includeNA = TRUE)
mwByE <- svyCreateTableOne(vars = vars, strata = c("E"), data = datSvy, factorVars = factorVars)
## 2020-02-29
## The following test fails in matrix inversion (appropriately) under R 3.6.2.
## svyCreateTableOne
## svyCreateContTable
## ModuleTestSafe (This hides error in testing intentionally).
## svyGlmTermTest
## regTermTest(svyglm(E ~ ..strataVar.. (ExC1 6 categories), design)), )
## beta <- coef(res)
## V <- vcov(res) # 6x6 all elements numerically zeros
## # This is expected as E ~ E*C1 has perfect prediction.
## chisq <- beta %*% solve(V) %*% beta # inversion by solve() fails
## Error in solve.default(V) :
## Lapack routine dgesv: system is exactly singular: U[4,4] = 0
## p value for row variable E will be NA. Previously, it was <0.001.
##
## R 3.6.1 (2019-07-05) no mention of solve(). 3 solve() related errors don't happen.
## R 3.6.2 (2019-12-12) improved solve() and may have changed the behavior.
##
## 2020-07-25
## If solve(Vmat) errors, the resulting p value is going to be NA. If not, <0.001.
Vmat <- structure(c(8.75501633118467e-34, 1.46773954995106e-33, 1.17162059153476e-33,
8.75501633118467e-34, 8.75501633118467e-34, 1.46773954995106e-33,
9.16683246877479e-33, 1.36903323047896e-33, 1.46773954995106e-33,
1.46773954995106e-33, 1.17162059153476e-33, 1.36903323047896e-33,
5.21857968989081e-33, 1.17162059153476e-33, 1.17162059153476e-33,
8.75501633118467e-34, 1.46773954995106e-33, 1.17162059153476e-33,
8.75501633118467e-34, 8.75501633118467e-34, 8.75501633118467e-34,
1.46773954995106e-33, 1.17162059153476e-33, 8.75501633118467e-34,
8.75501633118467e-34), .Dim = c(5L, 5L))
logical_na_expected <- (class(try(solve(Vmat)))[1] == "try-error")
mwByEC1 <- svyCreateTableOne(vars = vars, strata = c("E","C1"), data = datSvy, factorVars = factorVars)
mwContOnlyByEC1 <- svyCreateTableOne(vars = c("E","C"), strata = c("E","C1"), data = datSvy, factorVars = factorVars)
mwCatOnlyByEC1 <- svyCreateTableOne(vars = c("Y","C1"), strata = c("E","C1"), data = datSvy, factorVars = factorVars)
mwByE_addOverall <- svyCreateTableOne(vars = vars, strata = c("E"), data = datSvy, factorVars = factorVars, addOverall = TRUE)
mwByEC1_addOverall <- svyCreateTableOne(vars = vars, strata = c("E","C1"), data = datSvy, factorVars = factorVars, addOverall = TRUE)
## Specify variables for special handling
nonnormalVars <- c("C")
exactVars <- c("Y")
test_that("svyTableOne objects are always returned", {
expect_equal(class(mwOverall), c("svyTableOne","TableOne"))
expect_equal(class(mwInclNa), c("svyTableOne","TableOne"))
expect_equal(class(mwByE), c("svyTableOne","TableOne"))
expect_equal(class(mwByEC1), c("svyTableOne","TableOne"))
expect_equal(class(mwContOnlyByEC1), c("svyTableOne","TableOne"))
expect_equal(class(mwCatOnlyByEC1), c("svyTableOne","TableOne"))
## Extra-Test addOverall
expect_equal(class(mwByE_addOverall), c("svyTableOne","TableOne"))
expect_equal(class(mwByEC1_addOverall), c("svyTableOne","TableOne"))
})
test_that("Missing percentages are correctly stored and printed", {
## Extract from dataset
percentMissing <- unlist(lapply(datMw[vars], function(x) {sum(is.na(x)) / length(x) * 100}))
## Sanity check for the standard.
expect_equal(length(percentMissing), length(vars))
## Unstratified table
expect_equal(mwOverall$MetaData$percentMissing, percentMissing)
## Including NA as a category should not matter.
expect_equal(mwInclNa$MetaData$percentMissing, percentMissing)
## Stratification should not matter
expect_equal(mwByE$MetaData$percentMissing, percentMissing)
expect_equal(mwByEC1$MetaData$percentMissing, percentMissing)
## addOverall should not matter
expect_equal(mwByEC1_addOverall$MetaData$percentMissing, percentMissing)
expect_equal(mwByE_addOverall$MetaData$percentMissing, percentMissing)
## Check printing
## Gold standard
percentMissingString <- format(sprintf("%.1f", percentMissing), justify = "right")
## Function to drop empty elements like "" or " ".
DropEmptyString <- function(x) {
## as.character() drops names.
as.character(Filter(f = function(elt) {
!grepl("^ *$", elt)
},
x = x))
}
## Check against gold standard
expect_equal(DropEmptyString(print(mwOverall, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwInclNa, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByE, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByEC1, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByE_addOverall, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByEC1_addOverall, missing = TRUE)[,"Missing"]),
percentMissingString)
})
test_that("printing of a svyTableOne object does not regress", {
## Expectations
expect_equal_to_reference(print(mwByE, printToggle = TRUE),
"ref-svyTableOne_defaultPrint.rds")
expect_equal_to_reference(print(mwOverall, printToggle = TRUE),
"ref-svyTableOne_overallPrint.rds")
expect_equal_to_reference(print(mwInclNa, printToggle = TRUE),
"ref-svyTableOne_IncludeNA.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars.rds")
}
expect_equal_to_reference(print(mwByE, catDigits = 3, contDigits = 4, pDigits = 5, printToggle = TRUE),
"ref-svyTableOne_digits.rds")
expect_equal_to_reference(print(mwByE, test = FALSE, printToggle = TRUE),
"ref-svyTableOne_noTests.rds")
expect_equal_to_reference(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, printToggle = TRUE),
"ref-svyTableOne_nonnormal_exact.rds")
expect_equal_to_reference(print(mwByE, nonnormal = nonnormalVars, minMax = TRUE, printToggle = TRUE),
"ref-svyTableOne_nonnormal_minMax.rds")
expect_equal_to_reference(print(mwByE, catDigits = 3, noSpaces = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces.rds")
expect_equal_to_reference(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, showAllLevels = TRUE, printToggle = TRUE),
"ref-svyTableOne_showAllLevels.rds")
expect_equal_to_reference(print(mwByE, catDigits = 3, nonnormal = nonnormalVars, exact = exactVars, noSpaces = TRUE, showAllLevels = FALSE, quote = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces_showAllLevels_quote.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly_na.rds")
} else {
expect_equal_to_reference(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly.rds")
}
expect_equal_to_reference(print(mwCatOnlyByEC1),
"ref-svyTableOne_CatOnly.rds")
## Regression tests for addOverall
expect_equal_to_reference(print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_addOverall.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall.rds")
}
## Regression tests for formatOptions
expect_equal_to_reference(
print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_addOverall_formatOptions.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_na_formatOptions.rds")
} else {
expect_equal_to_reference(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_formatOptions.rds")
}
})
test_that("printing of a svyTableOne$CatTable object do not regress", {
## Expectations
expect_equal_to_reference(print(mwByE$CatTable, printToggle = TRUE),
"ref-svyCatTable_defaultPrint.rds")
expect_equal_to_reference(print(mwOverall$CatTable, printToggle = TRUE),
"ref-svyCatTable_overallPrint.rds")
expect_equal_to_reference(print(mwInclNa$CatTable, printToggle = TRUE),
"ref-svyCatTable_IncludeNA.rds")
expect_equal_to_reference(print(mwByEC1$CatTable, printToggle = TRUE),
"ref-svyCatTable_2StrataVars.rds")
expect_equal_to_reference(print(mwByEC1$CatTable, digits = 3, pDigits = 5, printToggle = TRUE),
"ref-svyCatTable_digits.rds")
expect_equal_to_reference(print(mwByEC1$CatTable, test = FALSE, printToggle = TRUE),
"ref-svyCatTable_noTests.rds")
expect_equal_to_reference(print(mwByE$CatTable, digits = 3, noSpaces = TRUE, printToggle = TRUE),
"ref-svyCatTable_noSpaces.rds")
expect_equal_to_reference(print(mwByE$CatTable, showAllLevels = TRUE, printToggle = TRUE),
"ref-svyCatTable_showAllLevels.rds")
expect_equal_to_reference(print(mwByE$CatTable, explain = FALSE, printToggle = TRUE),
"ref-svyCatTable_explain.rds")
expect_equal_to_reference(print(mwByE$CatTable, format = "f", printToggle = TRUE),
"ref-svyCatTable_format_f.rds")
expect_equal_to_reference(print(mwByE$CatTable, format = "p", printToggle = TRUE),
"ref-svyCatTable_format_p.rds")
expect_equal_to_reference(print(mwByE$CatTable, format = "pf", printToggle = TRUE),
"ref-svyCatTable_format_pf.rds")
expect_equal_to_reference(print(mwByE$CatTable, cramVars = "Y", printToggle = TRUE),
"ref-svyCatTable_cramVars.rds")
expect_equal_to_reference(print(mwByE$CatTable, noSpaces = TRUE, showAllLevels = TRUE, quote = TRUE, printToggle = TRUE),
"ref-svyCatTable_noSpaces_showAllLevels_quote.rds")
expect_equal_to_reference(print(mwByE_addOverall$CatTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyCatTable_addOverall.rds")
expect_equal_to_reference(print(mwByEC1_addOverall$CatTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyCatTable_2StrataVars_addOverall.rds")
## gmodels::CrossTable
print(mwByEC1$CatTable, CrossTable = TRUE)
expect_output(print(mwByEC1$CatTable, CrossTable = TRUE),
"|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|")
})
test_that("printing of a svyTableOne$ContTable object do not regress", {
## Expectations
expect_equal_to_reference(print(mwByE$ContTable, printToggle = TRUE),
"ref-svyContTable_defaultPrint.rds")
expect_equal_to_reference(print(mwOverall$ContTable, printToggle = TRUE),
"ref-svyContTable_overallPrint.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1$ContTable, printToggle = TRUE),
"ref-svyContTable_2StrataVars_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1$ContTable, printToggle = TRUE),
"ref-svyContTable_2StrataVars.rds")
}
expect_equal_to_reference(print(mwByE$ContTable, digits = 3, pDigits = 5, printToggle = TRUE),
"ref-svyContTable_digits.rds")
expect_equal_to_reference(print(mwByE$ContTable, test = FALSE, printToggle = TRUE),
"ref-svyContTable_noTests.rds")
expect_equal_to_reference(print(mwByE$ContTable, nonnormal = nonnormalVars, printToggle = TRUE),
"ref-svyContTable_nonnormal.rds")
expect_equal_to_reference(print(mwByE$ContTable, nonnormal = nonnormalVars, minMax = TRUE, printToggle = TRUE),
"ref-svyContTable_nonnormal_minMax.rds")
## This does not make a difference here
expect_equal_to_reference(print(mwByE$ContTable, noSpaces = TRUE, printToggle = TRUE),
"ref-svyContTable_noSpaces.rds")
expect_equal_to_reference(print(mwByE$ContTable, explain = FALSE, printToggle = TRUE),
"ref-svyContTable_explain.rds")
expect_equal_to_reference(print(mwByE$ContTable, noSpaces = TRUE, showAllLevels = TRUE, quote = TRUE, printToggle = TRUE),
"ref-svyContTable_noSpaces_showAllLevels_quote.rds")
expect_equal_to_reference(print(mwByE_addOverall$ContTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyContTable_addOverall.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1_addOverall$ContTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyContTable_2StrataVars_addOverall_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1_addOverall$ContTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyContTable_2StrataVars_addOverall.rds")
}
})
test_that("printing of a svyTableOne object does not regress (readable)", {
## Expectations
expect_known_output(print(mwByE, printToggle = TRUE),
"ref-svyTableOne_defaultPrint.txt")
expect_known_output(print(mwOverall, printToggle = TRUE),
"ref-svyTableOne_overallPrint.txt")
expect_known_output(print(mwInclNa, printToggle = TRUE),
"ref-svyTableOne_IncludeNA.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars_na.txt")
} else {
expect_known_output(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars.txt")
}
expect_known_output(print(mwByE, catDigits = 3, contDigits = 4, pDigits = 5, printToggle = TRUE),
"ref-svyTableOne_digits.txt")
expect_known_output(print(mwByE, test = FALSE, printToggle = TRUE),
"ref-svyTableOne_noTests.txt")
expect_known_output(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, printToggle = TRUE),
"ref-svyTableOne_nonnormal_exact.txt")
expect_known_output(print(mwByE, nonnormal = nonnormalVars, minMax = TRUE, printToggle = TRUE),
"ref-svyTableOne_nonnormal_minMax.txt")
expect_known_output(print(mwByE, catDigits = 3, noSpaces = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces.txt")
expect_known_output(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, showAllLevels = TRUE, printToggle = TRUE),
"ref-svyTableOne_showAllLevels.txt")
expect_known_output(print(mwByE, catDigits = 3, nonnormal = nonnormalVars, exact = exactVars, noSpaces = TRUE, showAllLevels = FALSE, quote = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces_showAllLevels_quote.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly_na.txt")
} else {
expect_known_output(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly.txt")
}
expect_known_output(print(mwCatOnlyByEC1),
"ref-svyTableOne_CatOnly.txt")
## Regression tests for addOverall
expect_known_output(print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_addOverall.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall_na.txt")
} else {
expect_known_output(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall.txt")
}
## Regression tests for formatOptions
expect_known_output(
print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_addOverall_formatOptions.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_na_formatOptions.txt")
} else {
expect_known_output(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_formatOptions.txt")
}
})
### p value calculations
test_that("p values are correctly calculated", {
## svychisq
pValuesTestChisq <- c(svychisq( ~ Y + E, datSvy)$p.value, svychisq( ~ C1 + E, datSvy)$p.value)
## Drop names X-squared X-squared
names(pValuesTestChisq) <- NULL
expect_equal(attr(mwByE$CatTable, "pValues")[, "pApprox"], pValuesTestChisq)
## no exact tests
expect_equal(attr(mwByE$CatTable, "pValues")[, "pExact"], c(NA, NA))
## svyglm to do ANOVA equivalent (first is removed to avoid singularity error on i386)
pValuesTestNormal <-
c(svyTestNormal("C ~ factor(E)", datSvy, test.terms = "factor(E)", method = "Wald")$p.value,
svyTestNormal("C2 ~ factor(E)", datSvy, test.terms = "factor(E)", method = "Wald")$p.value)
expect_equal(attr(mwByE$ContTable, "pValues")[, "pNormal"][-1], pValuesTestNormal)
expect_equal(attr(mwByE_addOverall$ContTable, "pValues")[, "pNormal"][-1], pValuesTestNormal)
## svyglm to do ANOVA equivalent
## Call stack
## svyTestNormal
## svyGlmTermTest
## survey::regTermTest
## solve
## solve.default
## solve.default() can error on some systems: i386, MLK, OpenBLAS
## system is computationally singular: reciprocal condition number = 5.45299e-17
##
## 2020-03-07 CRAN Additional issues on ATLAS MKL OpenBLAS likely for the same reason.
## Commenting out.
##
## if (R.Version()$arch != "i386") {
## expect_equal(attr(mwByE$ContTable, "pValues")[, "pNormal"][1],
## svyTestNormal("E ~ factor(E)", datSvy, test.terms = "factor(E)", method = "Wald")$p.value)
## }
## svyranktest
pValuesTestNonNormal <-
c(svyTestNonNormal("E ~ factor(E)", datSvy)$p.value,
svyTestNonNormal("C ~ factor(E)", datSvy)$p.value,
svyTestNonNormal("C2 ~ factor(E)", datSvy)$p.value)
expect_equal(attr(mwByE$ContTable, "pValues")[, "pNonNormal"], pValuesTestNonNormal)
expect_equal(attr(mwByE_addOverall$ContTable, "pValues")[, "pNonNormal"], pValuesTestNonNormal)
})
### Correctness of data object and final print out
################################################################################
## Create svydesign and TableOne object
data(nhanes)
nhanesSvy <- svydesign(ids = ~ SDMVPSU, strata = ~ SDMVSTRA, weights = ~ WTMEC2YR, nest = TRUE, data = nhanes)
tab1 <- svyCreateTableOne(vars = c("HI_CHOL","race","agecat","RIAGENDR"),
strata = "RIAGENDR", data = nhanesSvy,
factorVars = c("race","RIAGENDR"))
### Gold standar data
## Sample size
outTotals <- svyby(formula = ~ tableone:::one(HI_CHOL), by = ~ RIAGENDR, design = nhanesSvy,
FUN = svytotal, na.rm = TRUE)
## Means
outMeans <- svyby(formula = ~ HI_CHOL, by = ~ RIAGENDR, design = nhanesSvy, FUN = svymean, na.rm = TRUE)
## Variances (for SD)
outVar <- svyby(formula = ~ HI_CHOL, by = ~ RIAGENDR, design = nhanesSvy, FUN = svyvar, na.rm = TRUE)
## Quantiles
outQt1 <- svyquantile( ~ HI_CHOL, subset(nhanesSvy, RIAGENDR == 1), quantiles = c(0.5,0.25,0.75,0,1), na.rm = TRUE)
outQt2 <- svyquantile( ~ HI_CHOL, subset(nhanesSvy, RIAGENDR == 2), quantiles = c(0.5,0.25,0.75,0,1), na.rm = TRUE)
## Tests
pTTest <- sprintf(" %.7f", as.vector(svyttest(HI_CHOL ~ RIAGENDR, nhanesSvy)$p.value))
pRankTest <- sprintf(" %.7f", as.vector(svyranktest(HI_CHOL ~ RIAGENDR, nhanesSvy)$p.value))
test_that("continuous data object and final print outs are numerically correct", {
### Continuous part
### Sample sizes
## Correctness of one() function for sample size against total weights
expect_equal(as.vector(by(nhanes$WTMEC2YR, nhanes$RIAGENDR, sum)),
outTotals[,2])
## Numerical correctness
expect_equal(c(tab1$ContTable[[1]][,"n"], tab1$ContTable[[2]][,"n"]),
outTotals[,2])
## print out correctness
expect_equal(as.vector(print(tab1$ContTable, printToggle = TRUE)[1,1:2]),
sprintf("%.2f", outTotals[,2]))
### Means
## Numerical correctness
expect_equal(c(tab1$ContTable[[1]][,"mean"], tab1$ContTable[[2]][,"mean"]),
outMeans[,2])
### Standard deviations
## Numerically
expect_equal(c(tab1$ContTable[[1]][,"sd"], tab1$ContTable[[2]][,"sd"]),
sqrt(outVar[,2]))
## print out (mean and sd)
expect_equal(as.vector(print(tab1$ContTable, digits = 2, printToggle = TRUE)[2,1:2]),
sprintf("%.2f (%.2f)", outMeans[,2], sqrt(outVar[,2])))
### Quantiles
## median [p25, p75]
expect_equal(as.vector(print(tab1$ContTable, nonnormal = "HI_CHOL", printToggle = TRUE)[2,1:2]),
c(do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt1[1:3])),
do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt2[1:3]))))
## median [min,max]
expect_equal(as.vector(print(tab1$ContTable, nonnormal = "HI_CHOL", minMax = TRUE, printToggle = TRUE)[2,1:2]),
c(do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt1[c(1,4,5)])),
do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt2[c(1,4,5)]))))
### p-values
## t test
expect_equal(print(tab1$ContTable, pDigits = 7, printToggle = TRUE)[2,3],
## One space for <
pTTest)
## KW
expect_equal(print(tab1$ContTable, pDigits = 7, nonnormal = "HI_CHOL", printToggle = TRUE)[2,3],
## One space for <
pRankTest)
})
### Gold standard for categorical
outFreq <- svytable( ~ race + RIAGENDR, nhanesSvy)
outPerc <- prop.table(outFreq, margin = 2)
## Take first three rows only as space pading is tedious to reproduce
outFP <- cbind(sprintf("%.3f (%.3f) ", outFreq[,1], (outPerc[,1] * 100)),
sprintf(" %.3f ( %.3f) ", outFreq[,2], (outPerc[,2] * 100)))[1:3,]
outFP2 <- cbind(sprintf(" %.3f (%.3f) ", outFreq[,1], (outPerc[,1] * 100)),
sprintf(" %.3f ( %.3f) ", outFreq[,2], (outPerc[,2] * 100)))[1:3,]
## Test
pChisq <- sprintf(" %.7f", as.vector(svychisq( ~ race + RIAGENDR, nhanesSvy)$p.value))
test_that("categorical data object and final print outs are numerically correct", {
### Categorical part
### Sample sizes
expect_equal(as.vector(print(tab1$CatTable, digits = 3, printToggle = TRUE)[1,1:2]),
sprintf("%.3f", outTotals[,2]))
### Frequencies and percentages
matFP <- print(tab1$CatTable, digits = 3, printToggle = TRUE)[3:5,1:2]
dimnames(matFP) <- NULL
expect_equal(matFP, outFP)
### p-values
expect_equal(print(tab1$CatTable, pDigits = 7, printToggle = TRUE)[2,3],
pChisq)
})
test_that("mixed data object print outs are numerically correct", {
### Mixed object
## Mean and SD
expect_equal(as.vector(gsub("^ *", "", print(tab1, pDigits = 7)[2,1:2])),
sprintf("%.2f (%.2f)", outMeans[,2], sqrt(outVar[,2])))
## normal test
expect_equal(print(tab1, pDigits = 7)[2,3], pTTest)
## nonnormal test
expect_equal(print(tab1, pDigits = 7, nonnormal = "HI_CHOL")[2,3], pTTest)
## Table
matFP <- print(tab1, catDigits = 3, printToggle = TRUE)[4:6,1:2]
dimnames(matFP) <- NULL
## column by column deleting spaces
expect_equal(matFP[,1], outFP2[,1])
expect_equal(gsub(" ", "", matFP[,1]), gsub(" ", "", outFP[,1]))
## chisq test
expect_equal(print(tab1, pDigits = 7, nonnormal = "HI_CHOL")[3,3],
pChisq)
})
test_that("summary method works without errors", {
## Expectations
summary(mwOverall)
expect_output(summary(mwOverall), "### Summary of categorical variables ### ")
summary(mwInclNa)
expect_output(summary(mwInclNa), "<NA>")
summary(mwByE)
expect_output(summary(mwByE), "2 vs 3")
summary(mwByEC1)
expect_output(summary(mwByEC1),
"Standardize mean differences")
summary(mwContOnlyByEC1)
expect_output(summary(mwContOnlyByEC1),
"Standardize mean differences")
summary(mwCatOnlyByEC1)
expect_output(summary(mwCatOnlyByEC1),
"Standardize mean differences")
## AddOverall Test
expect_output(summary(mwByEC1_addOverall), "E:C1: Overall")
expect_output(summary(mwByEC1_addOverall), "E:C1: 3:1")
})
test_that("svyrep.design is allowed", {
### Replication weight data
## http://www.ats.ucla.edu/stat/stata/library/replicate_weights.htm
## https://r-survey.r-forge.r-project.org/survey/html/svrepdesign.html
## Survival in cardiac arrest (in survey)
data(scd)
scd
## use BRR replicate weights from Levy and Lemeshow
repweights <- 2 * cbind(c(1,0,1,0,1,0),
c(1,0,0,1,0,1),
c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
## Suppress the following warning.
## In svrepdesign.default(data = scd, type = "BRR", repweights = repweights, :
## No sampling weights provided: equal probability assumed
scdrep <- suppressWarnings(svrepdesign(data = scd, type = "BRR", repweights = repweights, combined.weights = FALSE))
## Standard construction
ans_means <- svyby(formula = ~ alive, by = ~ ESA, design = scdrep, FUN = svymean)[,2]
ans_sds <- sqrt(svyby(formula = ~ alive, by = ~ ESA, design = scdrep, FUN = svyvar)[,2])
ans_props <- svyby(formula = ~ I(ambulance - 1), by = ~ ESA, design = scdrep, FUN = svymean)[,2]
## Table construction
tab1 <- svyCreateTableOne(vars = c("alive", "ambulance"), strata = c("ESA"),
factorVars = "ambulance", data = scdrep)
tab1_print <- print(tab1, format = "p")
## Expectations
expect_equal(as.character(tab1_print[2, 1:3]),
sprintf("%.2f (%.2f)", ans_means, ans_sds))
expect_equal(as.character(gsub(" ", "", tab1_print[3, 1:3])),
sprintf("%.1f", ans_props * 100))
})
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################################################################################
### Unit tests for the svyCreateTableOne
## Reference: http://adv-r.had.co.nz/Testing.html
## Created on: 2015-07-24
## Author: Kazuki Yoshida
################################################################################
###
### Structure
## expectations within tests within context
###
### Prepare environment
################################################################################
library(testthat)
library(survey)
###
### Use oldsvyquantile when available
################################################################################
## 2020-07-30 via Thomas Lumley
## survey::svyquantile rewritten for survey 4.1
## To get the old behaviour you can simply call survey::oldsvyquantile() instead.
try_oldsvyquantile <- try(getFromNamespace(x = "oldsvyquantile", ns = "survey"))
if (is.function(try_oldsvyquantile)) {
svyquantile <- survey::oldsvyquantile
}
###
### Context (1 for each file)
################################################################################
context("Unit tests for svy* user functions")
###
### Provide data
################################################################################
## Three-group dataset with matching weight (Li & Greene 2013)
datMw <- structure(list(E = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), C = c(1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L), Y = c(0, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1), C1 = c(0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1
), C2 = c(0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,
0, 1, 1, 0, 0, 1, 1), n = c(252L, 28L, 105L, 45L, 12L, 28L, 1L,
9L, 72L, 8L, 63L, 27L, 24L, 56L, 1L, 9L, 36L, 4L, 42L, 18L, 24L,
56L, 8L, 72L), Mw = c(0.1429, 0.1429, 0.4, 0.4, 1, 1, 1, 1, 0.5,
0.5, 0.6667, 0.6667, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.125,
0.125)), class = "data.frame", .Names = c("E", "C", "Y", "C1",
"C2", "n", "Mw"), row.names = c(NA, -24L))
## Create individual level data
datMw <- datMw[rep(seq_along(datMw$n), datMw$n),]
## Introduce missingness
datMw[c(1,150), "Y"] <- NA
## Create a survey design object
datSvy <- svydesign(ids = ~ 1, data = datMw, weights = ~ Mw)
## Create a variable list
vars <- c("E", "C", "Y", "C1", "C2")
factorVars <- c("Y","C1")
###
### Tests
################################################################################
## A test should group together expectations for one functionality.
### Error handling
test_that("data assessment detects anomalies", {
## Error in ModuleStopIfNoVarsLeft(vars) (from modules.R#52) : No valid variables.
## In addition: Warning message:
## In ModuleReturnVarsExist(vars, data$variables) :
## The data frame does not have: none Dropped
expect_warning(expect_error(svyCreateTableOne(vars = "non-existent", data = datSvy),
"No valid variables"),
"The data frame does not have: non-existent Dropped")
## Error in StopIfNotSurveyDesign(data) :
## The data argument needs to be a survey design object.
expect_error(svyCreateTableOne(vars = c("E"), data = "not a svy object"),
"The data argument needs to be a survey design object")
## Data frame is not allowed
expect_error(svyCreateTableOne(vars = c("E"), data = data.frame(E = c(1,2,3))),
"The data argument needs to be a survey design object")
## Error in ModuleReturnStrata(strata, data$variable) (from modules.R#84) :
## None of the stratifying variables are present in the data frame.
## In addition: Warning message:
## In ModuleReturnVarsExist(strata, data) :
## The data frame does not have: non-existent Dropped
expect_warning(expect_error(svyCreateTableOne(vars = vars, strata = c("non-existent"), data = datSvy),
"None of the stratifying variables are present in the data frame"),
"The data frame does not have: non-existent Dropped")
})
### User-level functionalities
## Make categorical variables factors
## Create a table to test
mwOverall <- svyCreateTableOne(vars = vars, data = datSvy, factorVars = factorVars)
mwInclNa <- svyCreateTableOne(vars = vars, data = datSvy, factorVars = factorVars, includeNA = TRUE)
mwByE <- svyCreateTableOne(vars = vars, strata = c("E"), data = datSvy, factorVars = factorVars)
## 2020-02-29
## The following test fails in matrix inversion (appropriately) under R 3.6.2.
## svyCreateTableOne
## svyCreateContTable
## ModuleTestSafe (This hides error in testing intentionally).
## svyGlmTermTest
## regTermTest(svyglm(E ~ ..strataVar.. (ExC1 6 categories), design)), )
## beta <- coef(res)
## V <- vcov(res) # 6x6 all elements numerically zeros
## # This is expected as E ~ E*C1 has perfect prediction.
## chisq <- beta %*% solve(V) %*% beta # inversion by solve() fails
## Error in solve.default(V) :
## Lapack routine dgesv: system is exactly singular: U[4,4] = 0
## p value for row variable E will be NA. Previously, it was <0.001.
##
## R 3.6.1 (2019-07-05) no mention of solve(). 3 solve() related errors don't happen.
## R 3.6.2 (2019-12-12) improved solve() and may have changed the behavior.
##
## 2020-07-25
## If solve(Vmat) errors, the resulting p value is going to be NA. If not, <0.001.
Vmat <- structure(c(8.75501633118467e-34, 1.46773954995106e-33, 1.17162059153476e-33,
8.75501633118467e-34, 8.75501633118467e-34, 1.46773954995106e-33,
9.16683246877479e-33, 1.36903323047896e-33, 1.46773954995106e-33,
1.46773954995106e-33, 1.17162059153476e-33, 1.36903323047896e-33,
5.21857968989081e-33, 1.17162059153476e-33, 1.17162059153476e-33,
8.75501633118467e-34, 1.46773954995106e-33, 1.17162059153476e-33,
8.75501633118467e-34, 8.75501633118467e-34, 8.75501633118467e-34,
1.46773954995106e-33, 1.17162059153476e-33, 8.75501633118467e-34,
8.75501633118467e-34), .Dim = c(5L, 5L))
logical_na_expected <- (class(try(solve(Vmat)))[1] == "try-error")
mwByEC1 <- svyCreateTableOne(vars = vars, strata = c("E","C1"), data = datSvy, factorVars = factorVars)
mwContOnlyByEC1 <- svyCreateTableOne(vars = c("E","C"), strata = c("E","C1"), data = datSvy, factorVars = factorVars)
mwCatOnlyByEC1 <- svyCreateTableOne(vars = c("Y","C1"), strata = c("E","C1"), data = datSvy, factorVars = factorVars)
mwByE_addOverall <- svyCreateTableOne(vars = vars, strata = c("E"), data = datSvy, factorVars = factorVars, addOverall = TRUE)
mwByEC1_addOverall <- svyCreateTableOne(vars = vars, strata = c("E","C1"), data = datSvy, factorVars = factorVars, addOverall = TRUE)
## Specify variables for special handling
nonnormalVars <- c("C")
exactVars <- c("Y")
test_that("svyTableOne objects are always returned", {
expect_equal(class(mwOverall), c("svyTableOne","TableOne"))
expect_equal(class(mwInclNa), c("svyTableOne","TableOne"))
expect_equal(class(mwByE), c("svyTableOne","TableOne"))
expect_equal(class(mwByEC1), c("svyTableOne","TableOne"))
expect_equal(class(mwContOnlyByEC1), c("svyTableOne","TableOne"))
expect_equal(class(mwCatOnlyByEC1), c("svyTableOne","TableOne"))
## Extra-Test addOverall
expect_equal(class(mwByE_addOverall), c("svyTableOne","TableOne"))
expect_equal(class(mwByEC1_addOverall), c("svyTableOne","TableOne"))
})
test_that("Missing percentages are correctly stored and printed", {
## Extract from dataset
percentMissing <- unlist(lapply(datMw[vars], function(x) {sum(is.na(x)) / length(x) * 100}))
## Sanity check for the standard.
expect_equal(length(percentMissing), length(vars))
## Unstratified table
expect_equal(mwOverall$MetaData$percentMissing, percentMissing)
## Including NA as a category should not matter.
expect_equal(mwInclNa$MetaData$percentMissing, percentMissing)
## Stratification should not matter
expect_equal(mwByE$MetaData$percentMissing, percentMissing)
expect_equal(mwByEC1$MetaData$percentMissing, percentMissing)
## addOverall should not matter
expect_equal(mwByEC1_addOverall$MetaData$percentMissing, percentMissing)
expect_equal(mwByE_addOverall$MetaData$percentMissing, percentMissing)
## Check printing
## Gold standard
percentMissingString <- format(sprintf("%.1f", percentMissing), justify = "right")
## Function to drop empty elements like "" or " ".
DropEmptyString <- function(x) {
## as.character() drops names.
as.character(Filter(f = function(elt) {
!grepl("^ *$", elt)
},
x = x))
}
## Check against gold standard
expect_equal(DropEmptyString(print(mwOverall, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwInclNa, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByE, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByEC1, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByE_addOverall, missing = TRUE)[,"Missing"]),
percentMissingString)
expect_equal(DropEmptyString(print(mwByEC1_addOverall, missing = TRUE)[,"Missing"]),
percentMissingString)
})
test_that("printing of a svyTableOne object does not regress", {
## Expectations
expect_equal_to_reference(print(mwByE, printToggle = TRUE),
"ref-svyTableOne_defaultPrint.rds")
expect_equal_to_reference(print(mwOverall, printToggle = TRUE),
"ref-svyTableOne_overallPrint.rds")
expect_equal_to_reference(print(mwInclNa, printToggle = TRUE),
"ref-svyTableOne_IncludeNA.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars.rds")
}
expect_equal_to_reference(print(mwByE, catDigits = 3, contDigits = 4, pDigits = 5, printToggle = TRUE),
"ref-svyTableOne_digits.rds")
expect_equal_to_reference(print(mwByE, test = FALSE, printToggle = TRUE),
"ref-svyTableOne_noTests.rds")
expect_equal_to_reference(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, printToggle = TRUE),
"ref-svyTableOne_nonnormal_exact.rds")
expect_equal_to_reference(print(mwByE, nonnormal = nonnormalVars, minMax = TRUE, printToggle = TRUE),
"ref-svyTableOne_nonnormal_minMax.rds")
expect_equal_to_reference(print(mwByE, catDigits = 3, noSpaces = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces.rds")
expect_equal_to_reference(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, showAllLevels = TRUE, printToggle = TRUE),
"ref-svyTableOne_showAllLevels.rds")
expect_equal_to_reference(print(mwByE, catDigits = 3, nonnormal = nonnormalVars, exact = exactVars, noSpaces = TRUE, showAllLevels = FALSE, quote = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces_showAllLevels_quote.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly_na.rds")
} else {
expect_equal_to_reference(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly.rds")
}
expect_equal_to_reference(print(mwCatOnlyByEC1),
"ref-svyTableOne_CatOnly.rds")
## Regression tests for addOverall
expect_equal_to_reference(print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_addOverall.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall.rds")
}
## Regression tests for formatOptions
expect_equal_to_reference(
print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_addOverall_formatOptions.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_na_formatOptions.rds")
} else {
expect_equal_to_reference(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_formatOptions.rds")
}
})
test_that("printing of a svyTableOne$CatTable object do not regress", {
## Expectations
expect_equal_to_reference(print(mwByE$CatTable, printToggle = TRUE),
"ref-svyCatTable_defaultPrint.rds")
expect_equal_to_reference(print(mwOverall$CatTable, printToggle = TRUE),
"ref-svyCatTable_overallPrint.rds")
expect_equal_to_reference(print(mwInclNa$CatTable, printToggle = TRUE),
"ref-svyCatTable_IncludeNA.rds")
expect_equal_to_reference(print(mwByEC1$CatTable, printToggle = TRUE),
"ref-svyCatTable_2StrataVars.rds")
expect_equal_to_reference(print(mwByEC1$CatTable, digits = 3, pDigits = 5, printToggle = TRUE),
"ref-svyCatTable_digits.rds")
expect_equal_to_reference(print(mwByEC1$CatTable, test = FALSE, printToggle = TRUE),
"ref-svyCatTable_noTests.rds")
expect_equal_to_reference(print(mwByE$CatTable, digits = 3, noSpaces = TRUE, printToggle = TRUE),
"ref-svyCatTable_noSpaces.rds")
expect_equal_to_reference(print(mwByE$CatTable, showAllLevels = TRUE, printToggle = TRUE),
"ref-svyCatTable_showAllLevels.rds")
expect_equal_to_reference(print(mwByE$CatTable, explain = FALSE, printToggle = TRUE),
"ref-svyCatTable_explain.rds")
expect_equal_to_reference(print(mwByE$CatTable, format = "f", printToggle = TRUE),
"ref-svyCatTable_format_f.rds")
expect_equal_to_reference(print(mwByE$CatTable, format = "p", printToggle = TRUE),
"ref-svyCatTable_format_p.rds")
expect_equal_to_reference(print(mwByE$CatTable, format = "pf", printToggle = TRUE),
"ref-svyCatTable_format_pf.rds")
expect_equal_to_reference(print(mwByE$CatTable, cramVars = "Y", printToggle = TRUE),
"ref-svyCatTable_cramVars.rds")
expect_equal_to_reference(print(mwByE$CatTable, noSpaces = TRUE, showAllLevels = TRUE, quote = TRUE, printToggle = TRUE),
"ref-svyCatTable_noSpaces_showAllLevels_quote.rds")
expect_equal_to_reference(print(mwByE_addOverall$CatTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyCatTable_addOverall.rds")
expect_equal_to_reference(print(mwByEC1_addOverall$CatTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyCatTable_2StrataVars_addOverall.rds")
## gmodels::CrossTable
print(mwByEC1$CatTable, CrossTable = TRUE)
expect_output(print(mwByEC1$CatTable, CrossTable = TRUE),
"|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|")
})
test_that("printing of a svyTableOne$ContTable object do not regress", {
## Expectations
expect_equal_to_reference(print(mwByE$ContTable, printToggle = TRUE),
"ref-svyContTable_defaultPrint.rds")
expect_equal_to_reference(print(mwOverall$ContTable, printToggle = TRUE),
"ref-svyContTable_overallPrint.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1$ContTable, printToggle = TRUE),
"ref-svyContTable_2StrataVars_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1$ContTable, printToggle = TRUE),
"ref-svyContTable_2StrataVars.rds")
}
expect_equal_to_reference(print(mwByE$ContTable, digits = 3, pDigits = 5, printToggle = TRUE),
"ref-svyContTable_digits.rds")
expect_equal_to_reference(print(mwByE$ContTable, test = FALSE, printToggle = TRUE),
"ref-svyContTable_noTests.rds")
expect_equal_to_reference(print(mwByE$ContTable, nonnormal = nonnormalVars, printToggle = TRUE),
"ref-svyContTable_nonnormal.rds")
expect_equal_to_reference(print(mwByE$ContTable, nonnormal = nonnormalVars, minMax = TRUE, printToggle = TRUE),
"ref-svyContTable_nonnormal_minMax.rds")
## This does not make a difference here
expect_equal_to_reference(print(mwByE$ContTable, noSpaces = TRUE, printToggle = TRUE),
"ref-svyContTable_noSpaces.rds")
expect_equal_to_reference(print(mwByE$ContTable, explain = FALSE, printToggle = TRUE),
"ref-svyContTable_explain.rds")
expect_equal_to_reference(print(mwByE$ContTable, noSpaces = TRUE, showAllLevels = TRUE, quote = TRUE, printToggle = TRUE),
"ref-svyContTable_noSpaces_showAllLevels_quote.rds")
expect_equal_to_reference(print(mwByE_addOverall$ContTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyContTable_addOverall.rds")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_equal_to_reference(print(mwByEC1_addOverall$ContTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyContTable_2StrataVars_addOverall_na.rds")
} else {
expect_equal_to_reference(print(mwByEC1_addOverall$ContTable, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyContTable_2StrataVars_addOverall.rds")
}
})
test_that("printing of a svyTableOne object does not regress (readable)", {
## Expectations
expect_known_output(print(mwByE, printToggle = TRUE),
"ref-svyTableOne_defaultPrint.txt")
expect_known_output(print(mwOverall, printToggle = TRUE),
"ref-svyTableOne_overallPrint.txt")
expect_known_output(print(mwInclNa, printToggle = TRUE),
"ref-svyTableOne_IncludeNA.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars_na.txt")
} else {
expect_known_output(print(mwByEC1, printToggle = TRUE),
"ref-svyTableOne_2StrataVars.txt")
}
expect_known_output(print(mwByE, catDigits = 3, contDigits = 4, pDigits = 5, printToggle = TRUE),
"ref-svyTableOne_digits.txt")
expect_known_output(print(mwByE, test = FALSE, printToggle = TRUE),
"ref-svyTableOne_noTests.txt")
expect_known_output(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, printToggle = TRUE),
"ref-svyTableOne_nonnormal_exact.txt")
expect_known_output(print(mwByE, nonnormal = nonnormalVars, minMax = TRUE, printToggle = TRUE),
"ref-svyTableOne_nonnormal_minMax.txt")
expect_known_output(print(mwByE, catDigits = 3, noSpaces = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces.txt")
expect_known_output(print(mwByE, nonnormal = nonnormalVars, exact = exactVars, showAllLevels = TRUE, printToggle = TRUE),
"ref-svyTableOne_showAllLevels.txt")
expect_known_output(print(mwByE, catDigits = 3, nonnormal = nonnormalVars, exact = exactVars, noSpaces = TRUE, showAllLevels = FALSE, quote = TRUE, printToggle = TRUE),
"ref-svyTableOne_noSpaces_showAllLevels_quote.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly_na.txt")
} else {
expect_known_output(print(mwContOnlyByEC1),
"ref-svyTableOne_ContOnly.txt")
}
expect_known_output(print(mwCatOnlyByEC1),
"ref-svyTableOne_CatOnly.txt")
## Regression tests for addOverall
expect_known_output(print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_addOverall.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall_na.txt")
} else {
expect_known_output(print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE),
"ref-svyTableOne_2StrataVars_addOverall.txt")
}
## Regression tests for formatOptions
expect_known_output(
print(mwByE_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_addOverall_formatOptions.txt")
## 2020-02-29 Error due to solve() behavior change in R 3.6.2.
if (logical_na_expected) {
expect_known_output(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_na_formatOptions.txt")
} else {
expect_known_output(
print(mwByEC1_addOverall, printToggle = TRUE, test = TRUE, smd = TRUE,
formatOptions = list(big.mark = ",", big.interval = 2, decimal.mark = "D")),
"ref-svyTableOne_2StrataVars_addOverall_formatOptions.txt")
}
})
### p value calculations
test_that("p values are correctly calculated", {
## svychisq
pValuesTestChisq <- c(svychisq( ~ Y + E, datSvy)$p.value, svychisq( ~ C1 + E, datSvy)$p.value)
## Drop names X-squared X-squared
names(pValuesTestChisq) <- NULL
expect_equal(attr(mwByE$CatTable, "pValues")[, "pApprox"], pValuesTestChisq)
## no exact tests
expect_equal(attr(mwByE$CatTable, "pValues")[, "pExact"], c(NA, NA))
## svyglm to do ANOVA equivalent (first is removed to avoid singularity error on i386)
pValuesTestNormal <-
c(svyTestNormal("C ~ factor(E)", datSvy, test.terms = "factor(E)", method = "Wald")$p.value,
svyTestNormal("C2 ~ factor(E)", datSvy, test.terms = "factor(E)", method = "Wald")$p.value)
expect_equal(attr(mwByE$ContTable, "pValues")[, "pNormal"][-1], pValuesTestNormal)
expect_equal(attr(mwByE_addOverall$ContTable, "pValues")[, "pNormal"][-1], pValuesTestNormal)
## svyglm to do ANOVA equivalent
## Call stack
## svyTestNormal
## svyGlmTermTest
## survey::regTermTest
## solve
## solve.default
## solve.default() can error on some systems: i386, MLK, OpenBLAS
## system is computationally singular: reciprocal condition number = 5.45299e-17
##
## 2020-03-07 CRAN Additional issues on ATLAS MKL OpenBLAS likely for the same reason.
## Commenting out.
##
## if (R.Version()$arch != "i386") {
## expect_equal(attr(mwByE$ContTable, "pValues")[, "pNormal"][1],
## svyTestNormal("E ~ factor(E)", datSvy, test.terms = "factor(E)", method = "Wald")$p.value)
## }
## svyranktest
pValuesTestNonNormal <-
c(svyTestNonNormal("E ~ factor(E)", datSvy)$p.value,
svyTestNonNormal("C ~ factor(E)", datSvy)$p.value,
svyTestNonNormal("C2 ~ factor(E)", datSvy)$p.value)
expect_equal(attr(mwByE$ContTable, "pValues")[, "pNonNormal"], pValuesTestNonNormal)
expect_equal(attr(mwByE_addOverall$ContTable, "pValues")[, "pNonNormal"], pValuesTestNonNormal)
})
### Correctness of data object and final print out
################################################################################
## Create svydesign and TableOne object
data(nhanes)
nhanesSvy <- svydesign(ids = ~ SDMVPSU, strata = ~ SDMVSTRA, weights = ~ WTMEC2YR, nest = TRUE, data = nhanes)
tab1 <- svyCreateTableOne(vars = c("HI_CHOL","race","agecat","RIAGENDR"),
strata = "RIAGENDR", data = nhanesSvy,
factorVars = c("race","RIAGENDR"))
### Gold standar data
## Sample size
outTotals <- svyby(formula = ~ tableone:::one(HI_CHOL), by = ~ RIAGENDR, design = nhanesSvy,
FUN = svytotal, na.rm = TRUE)
## Means
outMeans <- svyby(formula = ~ HI_CHOL, by = ~ RIAGENDR, design = nhanesSvy, FUN = svymean, na.rm = TRUE)
## Variances (for SD)
outVar <- svyby(formula = ~ HI_CHOL, by = ~ RIAGENDR, design = nhanesSvy, FUN = svyvar, na.rm = TRUE)
## Quantiles
outQt1 <- svyquantile( ~ HI_CHOL, subset(nhanesSvy, RIAGENDR == 1), quantiles = c(0.5,0.25,0.75,0,1), na.rm = TRUE)
outQt2 <- svyquantile( ~ HI_CHOL, subset(nhanesSvy, RIAGENDR == 2), quantiles = c(0.5,0.25,0.75,0,1), na.rm = TRUE)
## Tests
pTTest <- sprintf(" %.7f", as.vector(svyttest(HI_CHOL ~ RIAGENDR, nhanesSvy)$p.value))
pRankTest <- sprintf(" %.7f", as.vector(svyranktest(HI_CHOL ~ RIAGENDR, nhanesSvy)$p.value))
test_that("continuous data object and final print outs are numerically correct", {
### Continuous part
### Sample sizes
## Correctness of one() function for sample size against total weights
expect_equal(as.vector(by(nhanes$WTMEC2YR, nhanes$RIAGENDR, sum)),
outTotals[,2])
## Numerical correctness
expect_equal(c(tab1$ContTable[[1]][,"n"], tab1$ContTable[[2]][,"n"]),
outTotals[,2])
## print out correctness
expect_equal(as.vector(print(tab1$ContTable, printToggle = TRUE)[1,1:2]),
sprintf("%.2f", outTotals[,2]))
### Means
## Numerical correctness
expect_equal(c(tab1$ContTable[[1]][,"mean"], tab1$ContTable[[2]][,"mean"]),
outMeans[,2])
### Standard deviations
## Numerically
expect_equal(c(tab1$ContTable[[1]][,"sd"], tab1$ContTable[[2]][,"sd"]),
sqrt(outVar[,2]))
## print out (mean and sd)
expect_equal(as.vector(print(tab1$ContTable, digits = 2, printToggle = TRUE)[2,1:2]),
sprintf("%.2f (%.2f)", outMeans[,2], sqrt(outVar[,2])))
### Quantiles
## median [p25, p75]
expect_equal(as.vector(print(tab1$ContTable, nonnormal = "HI_CHOL", printToggle = TRUE)[2,1:2]),
c(do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt1[1:3])),
do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt2[1:3]))))
## median [min,max]
expect_equal(as.vector(print(tab1$ContTable, nonnormal = "HI_CHOL", minMax = TRUE, printToggle = TRUE)[2,1:2]),
c(do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt1[c(1,4,5)])),
do.call(sprintf, c(list(fmt = "%.2f [%.2f, %.2f]"), outQt2[c(1,4,5)]))))
### p-values
## t test
expect_equal(print(tab1$ContTable, pDigits = 7, printToggle = TRUE)[2,3],
## One space for <
pTTest)
## KW
expect_equal(print(tab1$ContTable, pDigits = 7, nonnormal = "HI_CHOL", printToggle = TRUE)[2,3],
## One space for <
pRankTest)
})
### Gold standard for categorical
outFreq <- svytable( ~ race + RIAGENDR, nhanesSvy)
outPerc <- prop.table(outFreq, margin = 2)
## Take first three rows only as space pading is tedious to reproduce
outFP <- cbind(sprintf("%.3f (%.3f) ", outFreq[,1], (outPerc[,1] * 100)),
sprintf(" %.3f ( %.3f) ", outFreq[,2], (outPerc[,2] * 100)))[1:3,]
outFP2 <- cbind(sprintf(" %.3f (%.3f) ", outFreq[,1], (outPerc[,1] * 100)),
sprintf(" %.3f ( %.3f) ", outFreq[,2], (outPerc[,2] * 100)))[1:3,]
## Test
pChisq <- sprintf(" %.7f", as.vector(svychisq( ~ race + RIAGENDR, nhanesSvy)$p.value))
test_that("categorical data object and final print outs are numerically correct", {
### Categorical part
### Sample sizes
expect_equal(as.vector(print(tab1$CatTable, digits = 3, printToggle = TRUE)[1,1:2]),
sprintf("%.3f", outTotals[,2]))
### Frequencies and percentages
matFP <- print(tab1$CatTable, digits = 3, printToggle = TRUE)[3:5,1:2]
dimnames(matFP) <- NULL
expect_equal(matFP, outFP)
### p-values
expect_equal(print(tab1$CatTable, pDigits = 7, printToggle = TRUE)[2,3],
pChisq)
})
test_that("mixed data object print outs are numerically correct", {
### Mixed object
## Mean and SD
expect_equal(as.vector(gsub("^ *", "", print(tab1, pDigits = 7)[2,1:2])),
sprintf("%.2f (%.2f)", outMeans[,2], sqrt(outVar[,2])))
## normal test
expect_equal(print(tab1, pDigits = 7)[2,3], pTTest)
## nonnormal test
expect_equal(print(tab1, pDigits = 7, nonnormal = "HI_CHOL")[2,3], pTTest)
## Table
matFP <- print(tab1, catDigits = 3, printToggle = TRUE)[4:6,1:2]
dimnames(matFP) <- NULL
## column by column deleting spaces
expect_equal(matFP[,1], outFP2[,1])
expect_equal(gsub(" ", "", matFP[,1]), gsub(" ", "", outFP[,1]))
## chisq test
expect_equal(print(tab1, pDigits = 7, nonnormal = "HI_CHOL")[3,3],
pChisq)
})
test_that("summary method works without errors", {
## Expectations
summary(mwOverall)
expect_output(summary(mwOverall), "### Summary of categorical variables ### ")
summary(mwInclNa)
expect_output(summary(mwInclNa), "<NA>")
summary(mwByE)
expect_output(summary(mwByE), "2 vs 3")
summary(mwByEC1)
expect_output(summary(mwByEC1),
"Standardize mean differences")
summary(mwContOnlyByEC1)
expect_output(summary(mwContOnlyByEC1),
"Standardize mean differences")
summary(mwCatOnlyByEC1)
expect_output(summary(mwCatOnlyByEC1),
"Standardize mean differences")
## AddOverall Test
expect_output(summary(mwByEC1_addOverall), "E:C1: Overall")
expect_output(summary(mwByEC1_addOverall), "E:C1: 3:1")
})
test_that("svyrep.design is allowed", {
### Replication weight data
## http://www.ats.ucla.edu/stat/stata/library/replicate_weights.htm
## https://r-survey.r-forge.r-project.org/survey/html/svrepdesign.html
## Survival in cardiac arrest (in survey)
data(scd)
scd
## use BRR replicate weights from Levy and Lemeshow
repweights <- 2 * cbind(c(1,0,1,0,1,0),
c(1,0,0,1,0,1),
c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
## Suppress the following warning.
## In svrepdesign.default(data = scd, type = "BRR", repweights = repweights, :
## No sampling weights provided: equal probability assumed
scdrep <- suppressWarnings(svrepdesign(data = scd, type = "BRR", repweights = repweights, combined.weights = FALSE))
## Standard construction
ans_means <- svyby(formula = ~ alive, by = ~ ESA, design = scdrep, FUN = svymean)[,2]
ans_sds <- sqrt(svyby(formula = ~ alive, by = ~ ESA, design = scdrep, FUN = svyvar)[,2])
ans_props <- svyby(formula = ~ I(ambulance - 1), by = ~ ESA, design = scdrep, FUN = svymean)[,2]
## Table construction
tab1 <- svyCreateTableOne(vars = c("alive", "ambulance"), strata = c("ESA"),
factorVars = "ambulance", data = scdrep)
tab1_print <- print(tab1, format = "p")
## Expectations
expect_equal(as.character(tab1_print[2, 1:3]),
sprintf("%.2f (%.2f)", ans_means, ans_sds))
expect_equal(as.character(gsub(" ", "", tab1_print[3, 1:3])),
sprintf("%.1f", ans_props * 100))
})
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