tests/testthat/test_apa_print_htest.R
In crsh/papaja: Prepare American Psychological Association Journal Articles with R Markdown
context("apa_print.htest()")
test_that(
"t-Test for means"
, {
t_test <- t.test(extra ~ group, data = sleep)
t_test_output <- apa_print(t_test)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(t_test_output$stat, "$t(17.78) = -1.86$, $p = .079$")
expect_identical(t_test_output$est, "$\\Delta M = -1.58$, 95\\% CI $[-3.37, 0.21]$")
expect_identical(t_test_output$full, "$\\Delta M = -1.58$, 95\\% CI $[-3.37, 0.21]$, $t(17.78) = -1.86$, $p = .079$")
t_test <- t.test(extra ~ group, data = sleep, conf.level = .99)
t_test_output <- apa_print(t_test)
expect_apa_results(
object = t_test_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "99\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(
object = t_test_output$full_result
, expected = "$\\Delta M = -1.58$, 99\\% CI $[-4.03, 0.87]$, $t(17.78) = -1.86$, $p = .079$"
)
# t_test <- t.test(extra ~ group, data = sleep, var.equal = TRUE)
# t_test_output <- apa_print(t_test)
# expect_equal(t_test_output$stat, "$t(18) = -1.86$, $p = .079$")
# expect_equal(t_test_output$est, "$\\Delta M = 1.58$, 95\\% CI $[-3.36$, $0.20]$")
# expect_equal(t_test_output$full, "$\\Delta M = 1.58$, 95\\% CI $[-3.36$, $0.20]$, $t(18) = -1.86$, $p = .079$")
t_test <- with(
sleep
, t.test(
extra[group == "1"]
, extra[group == "2"]
, paired = TRUE
)
)
t_test_output <- apa_print(t_test)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$M_D$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(t_test_output$full, "$M_D = -1.58$, 95\\% CI $[-2.46, -0.70]$, $t(9) = -4.06$, $p = .003$")
t_test <- t.test(sleep$extra, mu = 0)
t_test_output <- apa_print(t_test)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$M$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(
t_test_output$full
, "$M = 1.54$, 95\\% CI $[0.60, 2.48]$, $t(19) = 3.41$, $p = .003$"
)
# Provide a custom ci, check values and labelling
ci <- structure(c(1.0, 2.0), "conf.level" = .7)
t_test_output <- apa_print(t_test, conf.int = ci)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$M$"
, conf.int = "70\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(t_test_output$est, "$M = 1.54$, 70\\% CI $[1.00, 2.00]$")
t_test_output <- apa_print(t_test, stat_name = "foobar")
expect_identical(t_test_output$stat, "$foobar(19) = 3.41$, $p = .003$")
t_test_output <- apa_print(t_test, est_name = "foobar")
expect_identical(t_test_output$est, "$foobar = 1.54$, 95\\% CI $[0.60, 2.48]$")
t_test_output <- apa_print(t_test, digits = 3)
expect_identical(t_test_output$est, "$M = 1.540$, 95\\% CI $[0.596, 2.484]$")
}
)
test_that(
"Wilcoxon tests"
, {
wilcox_test <- wilcox.test(extra ~ group, data = sleep, exact = FALSE)
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$stat, "$W = 25.50$, $p = .069$")
wilcox_test <- wilcox.test(extra ~ group, data = sleep, conf.int = TRUE, exact = FALSE)
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
estimate = "$\\Delta \\mathit{Mdn}$"
, conf.int = "95\\% CI"
, statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$est, "$\\Delta \\mathit{Mdn} = -1.35$, 95\\% CI $[-3.60, 0.10]$")
expect_identical(wilcox_test_output$full, "$\\Delta \\mathit{Mdn} = -1.35$, 95\\% CI $[-3.60, 0.10]$, $W = 25.50$, $p = .069$")
wilcox_test <- with(
sleep
, wilcox.test(
extra[group == "1"]
, extra[group == "2"]
, paired = TRUE
, exact = FALSE
)
)
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
statistic = "$V$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$stat, "$V = 0.00$, $p = .009$")
expect_identical(wilcox_test_output$full, wilcox_test_output$stat)
wilcox_test <- suppressWarnings(wilcox.test(sleep$extra, mu = 0, conf.int = TRUE, conf.level = .96))
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
estimate = "$\\mathit{Mdn}^*$"
, conf.int = "96\\% CI"
, statistic = "$V$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$full, "$\\mathit{Mdn}^* = 1.60$, 96\\% CI $[0.40, 2.70]$, $V = 162.50$, $p = .007$")
}
)
test_that(
"Tests for correlations"
, {
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)
# Pearson's correlation ----
cor_test <- cor.test(x, y)
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$r$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$stat, "$t(7) = 1.84$, $p = .108$")
expect_identical(cor_test_output$est, "$r = .57$, 95\\% CI $[-.15, .90]$")
expect_identical(cor_test_output$full, "$r = .57$, 95\\% CI $[-.15, .90]$, $t(7) = 1.84$, $p = .108$")
# Spearman's rank correlation ----
cor_test <- cor.test(x, y, method = "spearman")
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$r_{\\mathrm{s}}$"
, statistic = "$S$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$full, "$r_{\\mathrm{s}} = .60$, $S = 48.00$, $p = .097$")
# Kendall's tau, exact statistic ----
cor_test <- cor.test(x, y, method = "kendall")
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$\\uptau$"
, statistic = "$T$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$full, "$\\uptau = .44$, $T = 26.00$, $p = .119$")
# Kendall's tau with normal approcimation ----
cor_test <- cor.test(x, y, method = "kendall", exact = FALSE)
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$\\uptau$"
, statistic = "$z$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$full, "$\\uptau = .44$, $z = 1.67$, $p = .095$")
}
)
test_that(
"Chi-squared for contingency tables"
, {
prop_test <- prop.test(5, 10)
prop_test_output <- suppressWarnings(apa_print(prop_test, n = 10))
expect_apa_results(
prop_test_output
, labels = list(
estimate = "$\\hat\\pi$"
, conf.int = "95\\% CI"
, statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
prop_test <- prop.test(smokers, patients)
expect_error(apa_print(prop_test), "Please provide the sample size to report.")
prop_test_output <- suppressWarnings(apa_print(prop_test, n = sum(patients)))
expect_apa_results(
prop_test_output
, labels = list(
statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(prop_test_output$stat, "$\\chi^2(3, n = 397) = 12.60$, $p = .006$")
two_sample_prop_test <- suppressWarnings(prop.test(smokers[3:4], n = patients[3:4]))
two_sample_prop_test_output <- apa_print(two_sample_prop_test, n = sum(patients[3:4]))
expect_apa_results(
two_sample_prop_test_output
, labels = list(
estimate = "$\\Delta \\hat\\pi$"
, conf.int = "95\\% CI"
, statistic = "$\\chi^2$"
, df ="$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(
attr(two_sample_prop_test_output$table$statistic, "n")
, apa_num(as.integer(sum(patients[3:4])))
)
}
)
test_that(
"Binomial test"
, {
binom_test_output <- binom.test(10, 20, p = .8, conf.level = .9) |>
papaja::apa_print()
expect_apa_results(
binom_test_output
, labels = list(
estimate = "$\\hat\\pi$"
, conf.int = "90\\% CI"
, p.value = "$p$"
)
)
}
)
test_that(
"Bartlett test"
, {
bartlett_test <- bartlett.test(count ~ spray, data = InsectSprays)
bartlett_test_output <- apa_print(bartlett_test)
expect_apa_results(
bartlett_test_output
, labels = list(
statistic = "$K^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(bartlett_test_output$stat, "$K^2(5) = 25.96$, $p < .001$")
}
)
test_that(
"Mauchly test"
, {
tmp <- capture.output(utils::example(SSD))
mauchly_data <- data.frame(
deg = gl(3, 1, 6, labels = c(0, 4, 8))
, noise = gl(2, 3, 6, labels = c("A", "P"))
)
mauchly_test <- mauchly.test(mlmfit, X = ~ deg + noise, idata = mauchly_data)
mauchly_output <- apa_print(mauchly_test)
expect_apa_results(
mauchly_output
, labels = list(
statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(mauchly_output$stat, "$W = 0.89$, $p = .638$")
mauchly_test <- mauchly.test(mlmfit, M = ~ deg + noise, X = ~ noise, idata = mauchly_data)
mauchly_output <- apa_print(mauchly_test)
expect_apa_results(
mauchly_output
, labels = list(
statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(mauchly_output$stat, "$W = 0.96$, $p = .850$")
}
)
test_that(
"One-way ANOVA"
, {
oneway_test <- oneway.test(extra ~ group, data = sleep)
oneway_output <- apa_print(oneway_test)
expect_apa_results(
oneway_output
, labels = list(
statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, p.value = "$p$"
)
)
expect_identical(oneway_output$stat, "$F(1, 17.78) = 3.46$, $p = .079$")
expect_identical(oneway_output$full, oneway_output$stat)
}
)
# Test for issue #192, confidence interval was confused with infty and extraneous $ symbols
test_that(
"One-sided t test (with infty in CI)"
, {
t_out <- t.test(formula = yield ~ N, data = npk, alternative = "greater")
apa_out <- apa_print(t_out)
t2 <- t.test(formula = yield ~ N, data = npk, alternative = "less")
apa2 <- apa_print(t2)
expect_apa_results(apa_out, col.names = c("estimate" , "conf.int", "statistic", "df", "p.value"))
expect_apa_results(apa2, col.names = c("estimate", "conf.int", "statistic", "df", "p.value"))
# positive infinity ----
expect_identical(
object = apa_out$full_result
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-9.54, \\infty]$, $t(21.88) = -2.46$, $p = .989$"
)
expect_identical(
object = apa_out$estimate
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-9.54, \\infty]$"
)
expect_identical(
apa_out$table$conf.int
, expected = structure("[-9.54, $\\infty$]", label = "95\\% CI", class = c("tiny_labelled", "character"))
)
# negative infinity ----
expect_identical(
object = apa2$full_result
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-\\infty, -1.70]$, $t(21.88) = -2.46$, $p = .011$"
)
expect_identical(
object = apa2$estimate
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-\\infty, -1.70]$"
)
expect_identical(
apa2$table$conf.int
, expected = structure(
"[-$\\infty$, -1.70]"
, label = "95\\% CI"
, class = c("tiny_labelled", "character")
)
)
}
)
test_that(
"Degenerate htest objects"
, {
degenerate <- t.test(yield ~ N, npk)
degenerate$statistic <- degenerate$estimate <- NULL
expect_error(apa_print(degenerate, est_name = "M"), "No estimate available in results table.")
expect_error(apa_print(degenerate, stat_name = "t"), "No statistic available in results table.")
}
)
test_that(
"Deprecated 'ci' argument"
, {
expect_warning(
apa_print(t.test(yield ~ N, npk), ci = c(1, 2))
, regexp = "Using argument 'ci' in calls to 'apa_print()' is deprecated. Please use 'conf.int' instead."
, fixed = TRUE
)
}
)
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context("apa_print.htest()")
test_that(
"t-Test for means"
, {
t_test <- t.test(extra ~ group, data = sleep)
t_test_output <- apa_print(t_test)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(t_test_output$stat, "$t(17.78) = -1.86$, $p = .079$")
expect_identical(t_test_output$est, "$\\Delta M = -1.58$, 95\\% CI $[-3.37, 0.21]$")
expect_identical(t_test_output$full, "$\\Delta M = -1.58$, 95\\% CI $[-3.37, 0.21]$, $t(17.78) = -1.86$, $p = .079$")
t_test <- t.test(extra ~ group, data = sleep, conf.level = .99)
t_test_output <- apa_print(t_test)
expect_apa_results(
object = t_test_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "99\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(
object = t_test_output$full_result
, expected = "$\\Delta M = -1.58$, 99\\% CI $[-4.03, 0.87]$, $t(17.78) = -1.86$, $p = .079$"
)
# t_test <- t.test(extra ~ group, data = sleep, var.equal = TRUE)
# t_test_output <- apa_print(t_test)
# expect_equal(t_test_output$stat, "$t(18) = -1.86$, $p = .079$")
# expect_equal(t_test_output$est, "$\\Delta M = 1.58$, 95\\% CI $[-3.36$, $0.20]$")
# expect_equal(t_test_output$full, "$\\Delta M = 1.58$, 95\\% CI $[-3.36$, $0.20]$, $t(18) = -1.86$, $p = .079$")
t_test <- with(
sleep
, t.test(
extra[group == "1"]
, extra[group == "2"]
, paired = TRUE
)
)
t_test_output <- apa_print(t_test)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$M_D$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(t_test_output$full, "$M_D = -1.58$, 95\\% CI $[-2.46, -0.70]$, $t(9) = -4.06$, $p = .003$")
t_test <- t.test(sleep$extra, mu = 0)
t_test_output <- apa_print(t_test)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$M$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(
t_test_output$full
, "$M = 1.54$, 95\\% CI $[0.60, 2.48]$, $t(19) = 3.41$, $p = .003$"
)
# Provide a custom ci, check values and labelling
ci <- structure(c(1.0, 2.0), "conf.level" = .7)
t_test_output <- apa_print(t_test, conf.int = ci)
expect_apa_results(
t_test_output
, labels = list(
estimate = "$M$"
, conf.int = "70\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(t_test_output$est, "$M = 1.54$, 70\\% CI $[1.00, 2.00]$")
t_test_output <- apa_print(t_test, stat_name = "foobar")
expect_identical(t_test_output$stat, "$foobar(19) = 3.41$, $p = .003$")
t_test_output <- apa_print(t_test, est_name = "foobar")
expect_identical(t_test_output$est, "$foobar = 1.54$, 95\\% CI $[0.60, 2.48]$")
t_test_output <- apa_print(t_test, digits = 3)
expect_identical(t_test_output$est, "$M = 1.540$, 95\\% CI $[0.596, 2.484]$")
}
)
test_that(
"Wilcoxon tests"
, {
wilcox_test <- wilcox.test(extra ~ group, data = sleep, exact = FALSE)
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$stat, "$W = 25.50$, $p = .069$")
wilcox_test <- wilcox.test(extra ~ group, data = sleep, conf.int = TRUE, exact = FALSE)
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
estimate = "$\\Delta \\mathit{Mdn}$"
, conf.int = "95\\% CI"
, statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$est, "$\\Delta \\mathit{Mdn} = -1.35$, 95\\% CI $[-3.60, 0.10]$")
expect_identical(wilcox_test_output$full, "$\\Delta \\mathit{Mdn} = -1.35$, 95\\% CI $[-3.60, 0.10]$, $W = 25.50$, $p = .069$")
wilcox_test <- with(
sleep
, wilcox.test(
extra[group == "1"]
, extra[group == "2"]
, paired = TRUE
, exact = FALSE
)
)
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
statistic = "$V$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$stat, "$V = 0.00$, $p = .009$")
expect_identical(wilcox_test_output$full, wilcox_test_output$stat)
wilcox_test <- suppressWarnings(wilcox.test(sleep$extra, mu = 0, conf.int = TRUE, conf.level = .96))
wilcox_test_output <- apa_print(wilcox_test)
expect_apa_results(
wilcox_test_output
, labels = list(
estimate = "$\\mathit{Mdn}^*$"
, conf.int = "96\\% CI"
, statistic = "$V$"
, p.value = "$p$"
)
)
expect_identical(wilcox_test_output$full, "$\\mathit{Mdn}^* = 1.60$, 96\\% CI $[0.40, 2.70]$, $V = 162.50$, $p = .007$")
}
)
test_that(
"Tests for correlations"
, {
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)
# Pearson's correlation ----
cor_test <- cor.test(x, y)
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$r$"
, conf.int = "95\\% CI"
, statistic = "$t$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$stat, "$t(7) = 1.84$, $p = .108$")
expect_identical(cor_test_output$est, "$r = .57$, 95\\% CI $[-.15, .90]$")
expect_identical(cor_test_output$full, "$r = .57$, 95\\% CI $[-.15, .90]$, $t(7) = 1.84$, $p = .108$")
# Spearman's rank correlation ----
cor_test <- cor.test(x, y, method = "spearman")
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$r_{\\mathrm{s}}$"
, statistic = "$S$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$full, "$r_{\\mathrm{s}} = .60$, $S = 48.00$, $p = .097$")
# Kendall's tau, exact statistic ----
cor_test <- cor.test(x, y, method = "kendall")
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$\\uptau$"
, statistic = "$T$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$full, "$\\uptau = .44$, $T = 26.00$, $p = .119$")
# Kendall's tau with normal approcimation ----
cor_test <- cor.test(x, y, method = "kendall", exact = FALSE)
cor_test_output <- apa_print(cor_test)
expect_apa_results(
cor_test_output
, labels = list(
estimate = "$\\uptau$"
, statistic = "$z$"
, p.value = "$p$"
)
)
expect_identical(cor_test_output$full, "$\\uptau = .44$, $z = 1.67$, $p = .095$")
}
)
test_that(
"Chi-squared for contingency tables"
, {
prop_test <- prop.test(5, 10)
prop_test_output <- suppressWarnings(apa_print(prop_test, n = 10))
expect_apa_results(
prop_test_output
, labels = list(
estimate = "$\\hat\\pi$"
, conf.int = "95\\% CI"
, statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
prop_test <- prop.test(smokers, patients)
expect_error(apa_print(prop_test), "Please provide the sample size to report.")
prop_test_output <- suppressWarnings(apa_print(prop_test, n = sum(patients)))
expect_apa_results(
prop_test_output
, labels = list(
statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(prop_test_output$stat, "$\\chi^2(3, n = 397) = 12.60$, $p = .006$")
two_sample_prop_test <- suppressWarnings(prop.test(smokers[3:4], n = patients[3:4]))
two_sample_prop_test_output <- apa_print(two_sample_prop_test, n = sum(patients[3:4]))
expect_apa_results(
two_sample_prop_test_output
, labels = list(
estimate = "$\\Delta \\hat\\pi$"
, conf.int = "95\\% CI"
, statistic = "$\\chi^2$"
, df ="$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(
attr(two_sample_prop_test_output$table$statistic, "n")
, apa_num(as.integer(sum(patients[3:4])))
)
}
)
test_that(
"Binomial test"
, {
binom_test_output <- binom.test(10, 20, p = .8, conf.level = .9) |>
papaja::apa_print()
expect_apa_results(
binom_test_output
, labels = list(
estimate = "$\\hat\\pi$"
, conf.int = "90\\% CI"
, p.value = "$p$"
)
)
}
)
test_that(
"Bartlett test"
, {
bartlett_test <- bartlett.test(count ~ spray, data = InsectSprays)
bartlett_test_output <- apa_print(bartlett_test)
expect_apa_results(
bartlett_test_output
, labels = list(
statistic = "$K^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
expect_identical(bartlett_test_output$stat, "$K^2(5) = 25.96$, $p < .001$")
}
)
test_that(
"Mauchly test"
, {
tmp <- capture.output(utils::example(SSD))
mauchly_data <- data.frame(
deg = gl(3, 1, 6, labels = c(0, 4, 8))
, noise = gl(2, 3, 6, labels = c("A", "P"))
)
mauchly_test <- mauchly.test(mlmfit, X = ~ deg + noise, idata = mauchly_data)
mauchly_output <- apa_print(mauchly_test)
expect_apa_results(
mauchly_output
, labels = list(
statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(mauchly_output$stat, "$W = 0.89$, $p = .638$")
mauchly_test <- mauchly.test(mlmfit, M = ~ deg + noise, X = ~ noise, idata = mauchly_data)
mauchly_output <- apa_print(mauchly_test)
expect_apa_results(
mauchly_output
, labels = list(
statistic = "$W$"
, p.value = "$p$"
)
)
expect_identical(mauchly_output$stat, "$W = 0.96$, $p = .850$")
}
)
test_that(
"One-way ANOVA"
, {
oneway_test <- oneway.test(extra ~ group, data = sleep)
oneway_output <- apa_print(oneway_test)
expect_apa_results(
oneway_output
, labels = list(
statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, p.value = "$p$"
)
)
expect_identical(oneway_output$stat, "$F(1, 17.78) = 3.46$, $p = .079$")
expect_identical(oneway_output$full, oneway_output$stat)
}
)
# Test for issue #192, confidence interval was confused with infty and extraneous $ symbols
test_that(
"One-sided t test (with infty in CI)"
, {
t_out <- t.test(formula = yield ~ N, data = npk, alternative = "greater")
apa_out <- apa_print(t_out)
t2 <- t.test(formula = yield ~ N, data = npk, alternative = "less")
apa2 <- apa_print(t2)
expect_apa_results(apa_out, col.names = c("estimate" , "conf.int", "statistic", "df", "p.value"))
expect_apa_results(apa2, col.names = c("estimate", "conf.int", "statistic", "df", "p.value"))
# positive infinity ----
expect_identical(
object = apa_out$full_result
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-9.54, \\infty]$, $t(21.88) = -2.46$, $p = .989$"
)
expect_identical(
object = apa_out$estimate
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-9.54, \\infty]$"
)
expect_identical(
apa_out$table$conf.int
, expected = structure("[-9.54, $\\infty$]", label = "95\\% CI", class = c("tiny_labelled", "character"))
)
# negative infinity ----
expect_identical(
object = apa2$full_result
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-\\infty, -1.70]$, $t(21.88) = -2.46$, $p = .011$"
)
expect_identical(
object = apa2$estimate
, expected = "$\\Delta M = -5.62$, 95\\% CI $[-\\infty, -1.70]$"
)
expect_identical(
apa2$table$conf.int
, expected = structure(
"[-$\\infty$, -1.70]"
, label = "95\\% CI"
, class = c("tiny_labelled", "character")
)
)
}
)
test_that(
"Degenerate htest objects"
, {
degenerate <- t.test(yield ~ N, npk)
degenerate$statistic <- degenerate$estimate <- NULL
expect_error(apa_print(degenerate, est_name = "M"), "No estimate available in results table.")
expect_error(apa_print(degenerate, stat_name = "t"), "No statistic available in results table.")
}
)
test_that(
"Deprecated 'ci' argument"
, {
expect_warning(
apa_print(t.test(yield ~ N, npk), ci = c(1, 2))
, regexp = "Using argument 'ci' in calls to 'apa_print()' is deprecated. Please use 'conf.int' instead."
, fixed = TRUE
)
}
)
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