Nothing
tests/testthat/test_apa_print_anova.R
In papaja: Prepare American Psychological Association Journal Articles with R Markdown
context("apa_print() for ANOVA")
# Data and examples from http://personality-project.org/r/r.guide.html#anova
# Use our own effect-size function for these tests
# Custom effect sizes via the 'effectsize' package are tested elsewhere
op <- options(papaja.estimate_anova = "ges", papaja.mse = TRUE)
test_that(
"One-way between ANOVA"
, {
load("data/ow_data.rdata")
ow_aov <- aov(Alertness ~ Dosage, data = ow_data)
ow_aov_output <- apa_print(ow_aov)
expect_apa_results(
ow_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(ow_aov_output$stat), "Dosage")
expect_identical(ow_aov_output$stat$Dosage, "$F(2, 15) = 8.79$, $\\mathit{MSE} = 24.25$, $p = .003$")
# est
expect_identical(names(ow_aov_output$est), "Dosage")
expect_identical(ow_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .540$")
# full
expect_identical(names(ow_aov_output$full), "Dosage")
expect_identical(ow_aov_output$full$Dosage, "$F(2, 15) = 8.79$, $\\mathit{MSE} = 24.25$, $p = .003$, $\\hat{\\eta}^2_G = .540$")
# table
expect_identical(nrow(ow_aov_output$table), 1L)
# Other classes
ow_aov_summary_output <- apa_print(summary(ow_aov))
expect_identical(ow_aov_summary_output, ow_aov_output)
ow_aov_Anova_output <- apa_print(car::Anova(ow_aov))
expect_identical(ow_aov_Anova_output, ow_aov_output)
ow_afex_data <- cbind(id = 1:nrow(ow_data), ow_data)
ow_afex_aov <- afex::aov_ez(
data = ow_afex_data
, id = "id"
, dv = "Alertness"
, between = "Dosage"
)
ow_afex_aov_output <- apa_print(ow_afex_aov$aov)
expect_identical(ow_afex_aov_output, ow_aov_output)
ow_afex_aov_output <- apa_print(ow_afex_aov$Anova)
expect_identical(ow_afex_aov_output, ow_aov_output)
ow_afex_aov_output <- apa_print(ow_afex_aov)
expect_identical(ow_afex_aov_output, ow_aov_output)
# With intercept
ow_afex_aov_output <- apa_print(ow_afex_aov$Anova, intercept = TRUE)
expect_apa_results(
ow_afex_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(ow_afex_aov_output$stat), c("Intercept", "Dosage"))
expect_is(ow_afex_aov_output$stat$Intercept, "character")
expect_identical(ow_afex_aov_output$stat$Intercept, "$F(1, 15) = 487.23$, $\\mathit{MSE} = 24.25$, $p < .001$")
# est
expect_identical(names(ow_afex_aov_output$est), c("Intercept", "Dosage"))
expect_is(ow_afex_aov_output$est$Intercept, "character")
expect_identical(ow_afex_aov_output$est$Intercept, "$\\hat{\\eta}^2_G = .970$")
# full
expect_identical(names(ow_afex_aov_output$full), c("Intercept", "Dosage"))
expect_is(ow_afex_aov_output$full$Intercept, "character")
expect_identical(ow_afex_aov_output$full$Intercept, "$F(1, 15) = 487.23$, $\\mathit{MSE} = 24.25$, $p < .001$, $\\hat{\\eta}^2_G = .970$")
# table
expect_identical(nrow(ow_afex_aov_output$table), 2L)
expect_identical(
ow_afex_aov_output$table$term
, structure(
c("Intercept", "Dosage")
, class = c("tiny_labelled", "character")
, label = "Effect"
)
)
ow_afex_aov_output2 <- apa_print(ow_afex_aov, intercept = TRUE)
expect_identical(ow_afex_aov_output2, ow_afex_aov_output)
# Other effect sizes
ow_aov_output <- apa_print(ow_aov, es = "pes")
expect_identical(ow_aov_output$est$Dosage, "$\\hat{\\eta}^2_p = .540$")
ow_aov_output <- apa_print(ow_aov, es = c("ges"))
expect_apa_results(ow_aov_output)
expect_identical(ow_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .540$")
}
)
test_that(
"Two-way between ANOVA"
, {
load("data/tw_data.rdata")
tw_aov <- aov(Alertness ~ Gender * Dosage, tw_data)
tw_aov_output <- apa_print(tw_aov)
expect_apa_results(
tw_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(tw_aov_output$stat), c("Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_aov_output$stat$Gender, "$F(1, 12) = 2.95$, $\\mathit{MSE} = 25.94$, $p = .111$")
expect_identical(tw_aov_output$stat$Dosage, "$F(1, 12) = 0.20$, $\\mathit{MSE} = 25.94$, $p = .666$")
expect_identical(tw_aov_output$stat$Gender_Dosage, "$F(1, 12) = 0.00$, $\\mathit{MSE} = 25.94$, $p = .962$")
# est
expect_identical(names(tw_aov_output$est), c("Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_aov_output$est$Gender, "$\\hat{\\eta}^2_G = .197$")
expect_identical(tw_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .016$")
expect_identical(tw_aov_output$est$Gender_Dosage, "$\\hat{\\eta}^2_G = .000$")
# full
expect_identical(names(tw_aov_output$full), c("Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_aov_output$full$Gender, "$F(1, 12) = 2.95$, $\\mathit{MSE} = 25.94$, $p = .111$, $\\hat{\\eta}^2_G = .197$")
expect_identical(tw_aov_output$full$Dosage, "$F(1, 12) = 0.20$, $\\mathit{MSE} = 25.94$, $p = .666$, $\\hat{\\eta}^2_G = .016$")
expect_identical(tw_aov_output$full$Gender_Dosage, "$F(1, 12) = 0.00$, $\\mathit{MSE} = 25.94$, $p = .962$, $\\hat{\\eta}^2_G = .000$")
# table
expect_identical(nrow(tw_aov_output$table), 3L)
expect_identical(
tw_aov_output$table$term
, expected = structure(
c("Gender", "Dosage", "Gender $\\times$ Dosage")
, label = "Effect"
, class = c("tiny_labelled", "character")
)
)
# Other classes
tw_aov_Anova_output <- apa_print(car::Anova(tw_aov))
expect_identical(tw_aov_Anova_output, tw_aov_output)
tw_aov_summary_output <- apa_print(summary(tw_aov))
expect_identical(tw_aov_summary_output, tw_aov_output)
tw_afex_data <- cbind(id = 1:nrow(tw_data), tw_data)
tw_afex_aov <- afex::aov_ez(
data = tw_afex_data
, id = "id"
, dv = "Alertness"
, between = c("Gender", "Dosage")
)
tw_afex_aov_output <- apa_print(tw_afex_aov$aov)
expect_identical(tw_afex_aov_output, tw_aov_output)
# With intercept
tw_afex_aov_output <- apa_print(tw_afex_aov$Anova, intercept = TRUE)
expect_apa_results(
tw_afex_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
## stat
expect_identical(names(tw_afex_aov_output$stat), c("Intercept", "Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_afex_aov_output$stat$Intercept, "$F(1, 12) = 121.99$, $\\mathit{MSE} = 25.94$, $p < .001$")
## est
expect_identical(names(tw_afex_aov_output$est), c("Intercept", "Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_afex_aov_output$est$Intercept, "$\\hat{\\eta}^2_G = .910$")
## full
expect_identical(names(tw_afex_aov_output$full), c("Intercept", "Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_afex_aov_output$full$Intercept, "$F(1, 12) = 121.99$, $\\mathit{MSE} = 25.94$, $p < .001$, $\\hat{\\eta}^2_G = .910$")
## table
expect_identical(nrow(tw_afex_aov_output$table), 4L)
expect_identical(
object = tw_afex_aov_output$table$term
, expected = structure(
c("Intercept", "Gender", "Dosage", "Gender $\\times$ Dosage")
, label = "Effect"
, class = c("tiny_labelled", "character")
)
)
tw_afex_aov_output2 <- apa_print(tw_afex_aov, intercept = TRUE)
expect_identical(tw_afex_aov_output2, tw_afex_aov_output)
# Observed effects
tw_rm_aov_output <- apa_print(tw_aov, observed = "Gender")
expect_identical(tw_rm_aov_output$est$Gender, "$\\hat{\\eta}^2_G = .197$")
expect_identical(tw_rm_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .013$")
expect_identical(tw_rm_aov_output$est$Gender_Dosage, "$\\hat{\\eta}^2_G = .000$")
}
)
test_that(
"One-way repeated-measures ANOVA"
, {
options(papaja.estimate_anova = "ges")
load("data/rm_data.rdata")
rm_aov <- aov(Recall ~ Valence + Error(Subject/Valence), rm_data)
rm_aov_output <- apa_print(rm_aov)
expect_apa_results(
rm_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(rm_aov_output$stat), "Valence")
expect_identical(rm_aov_output$stat$Valence, "$F(2, 8) = 189.11$, $\\mathit{MSE} = 5.37$, $p < .001$")
# est
expect_identical(names(rm_aov_output$est), "Valence")
expect_identical(rm_aov_output$est$Valence, "$\\hat{\\eta}^2_G = .932$")
# full
expect_identical(names(rm_aov_output$full), "Valence")
expect_identical(rm_aov_output$full$Valence, "$F(2, 8) = 189.11$, $\\mathit{MSE} = 5.37$, $p < .001$, $\\hat{\\eta}^2_G = .932$")
# Other classes
rm_aov_summary_output <- apa_print(summary(rm_aov))
expect_identical(rm_aov_summary_output, rm_aov_output)
rm_afex_aov <- afex::aov_ez(
data = rm_data
, id = "Subject"
, dv = "Recall"
, within = "Valence"
)
rm_afex_aov_output <- apa_print(rm_afex_aov$aov)
expect_identical(rm_afex_aov_output, rm_aov_output)
rm_afex_aov_output <- apa_print(rm_afex_aov$Anova, correction = "none", intercept = FALSE)
expect_identical(rm_afex_aov_output, rm_aov_output)
rm_afex_aov_output <- apa_print(rm_afex_aov, correction = "none", intercept = FALSE)
expect_identical(rm_afex_aov_output, rm_aov_output)
# DF corrections
rm_afex_anova.mlm_output <- apa_print(rm_afex_aov$Anova, correction = "GG")
expect_apa_results(
rm_afex_anova.mlm_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}^{\\mathrm{GG}}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}^{\\mathrm{GG}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
expect_identical(rm_afex_anova.mlm_output$full$Valence, "$F(1.15, 4.60) = 189.11$, $\\mathit{MSE} = 9.34$, $p < .001$, $\\hat{\\eta}^2_G = .932$")
rm_afex_anova.mlm_output <- apa_print(rm_afex_aov$Anova, correction = "HF")
expect_apa_results(
rm_afex_anova.mlm_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}^{\\mathrm{HF}}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}^{\\mathrm{HF}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
expect_identical(rm_afex_anova.mlm_output$full$Valence, "$F(1.32, 5.26) = 189.11$, $\\mathit{MSE} = 8.16$, $p < .001$, $\\hat{\\eta}^2_G = .932$")
rm_afex_anova.mlm_output <- apa_print(rm_afex_aov$Anova, correction = "none")
expect_identical(rm_afex_anova.mlm_output$full$Valence, rm_aov_output$full$Valence)
}
)
test_that(
"Two-way repeated-measures ANOVA"
, {
load("data/tw_rm_data.rdata")
tw_rm_aov <- aov(Recall~ (Task * Valence) + Error(Subject/(Task * Valence)), tw_rm_data)
tw_rm_aov_output <- apa_print(tw_rm_aov)
expect_apa_results(
tw_rm_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(tw_rm_aov_output$stat), c("Task", "Valence", "Task_Valence"))
expect_identical(tw_rm_aov_output$stat$Task, "$F(1, 4) = 7.35$, $\\mathit{MSE} = 4.08$, $p = .054$")
expect_identical(tw_rm_aov_output$stat$Valence, "$F(2, 8) = 1.46$, $\\mathit{MSE} = 3.36$, $p = .288$")
expect_identical(tw_rm_aov_output$stat$Task_Valence, "$F(2, 8) = 0.29$, $\\mathit{MSE} = 2.41$, $p = .755$")
# est
expect_identical(names(tw_rm_aov_output$est), c("Task", "Valence", "Task_Valence"))
expect_identical(tw_rm_aov_output$est$Task, "$\\hat{\\eta}^2_G = .068$")
expect_identical(tw_rm_aov_output$est$Valence, "$\\hat{\\eta}^2_G = .023$")
expect_identical(tw_rm_aov_output$est$Task_Valence, "$\\hat{\\eta}^2_G = .003$")
# full
expect_identical(names(tw_rm_aov_output$full), c("Task", "Valence", "Task_Valence"))
expect_identical(tw_rm_aov_output$full$Task, "$F(1, 4) = 7.35$, $\\mathit{MSE} = 4.08$, $p = .054$, $\\hat{\\eta}^2_G = .068$")
expect_identical(tw_rm_aov_output$full$Valence, "$F(2, 8) = 1.46$, $\\mathit{MSE} = 3.36$, $p = .288$, $\\hat{\\eta}^2_G = .023$")
expect_identical(tw_rm_aov_output$full$Task_Valence, "$F(2, 8) = 0.29$, $\\mathit{MSE} = 2.41$, $p = .755$, $\\hat{\\eta}^2_G = .003$")
# Other classes
tw_rm_aov_summary_output <- apa_print(summary(tw_rm_aov))
expect_identical(tw_rm_aov_summary_output, tw_rm_aov_output)
tw_rm_afex_aov <- suppressWarnings(afex::aov_ez(
data = tw_rm_data
, id = "Subject"
, dv = "Recall"
, within = c("Task", "Valence")
))
tw_rm_afex_aov_output <- apa_print(tw_rm_afex_aov$aov)
expect_identical(tw_rm_afex_aov_output, tw_rm_aov_output)
tw_rm_afex_aov_output <- apa_print(tw_rm_afex_aov$Anova, correction = "none")
expect_identical(tw_rm_afex_aov_output, tw_rm_aov_output)
tw_rm_afex_aov_output <- suppressWarnings(apa_print(tw_rm_afex_aov, correction = "none"))
expect_identical(tw_rm_afex_aov_output, tw_rm_aov_output)
# Observed
tw_rm_aov_output <- apa_print(tw_rm_aov, observed = "Task")
expect_identical(tw_rm_aov_output$est$Task, "$\\hat{\\eta}^2_G = .068$")
expect_identical(tw_rm_aov_output$est$Valence, "$\\hat{\\eta}^2_G = .022$")
expect_identical(tw_rm_aov_output$est$Task_Valence, "$\\hat{\\eta}^2_G = .003$")
}
)
test_that(
"Levene test"
, {
load("data/ow_data.rdata")
levene_test <- car::leveneTest(Alertness ~ Dosage, data = ow_data)
levene_test_output <- apa_print(levene_test)
expect_apa_results(
levene_test_output
, labels = list(
statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, p.value = "$p$"
)
)
expect_identical(levene_test_output$stat, "$F(2, 15) = 4.17$, $p = .036$")
}
)
test_that(
"anova(lm(...))"
, {
model <- anova(lm(yield ~ N, data = npk))
apa_out <- apa_print(model)
expect_identical(
apa_out$table
, expected = structure(
list(
term = structure("N", label = "Effect", class = c("tiny_labelled", "character"))
, estimate = structure(".216", label = "$\\hat{\\eta}^2_G$", class = c("tiny_labelled","character"))
, statistic = structure("6.06", label = "$F$", class = c("tiny_labelled", "character"))
, df = structure("1", label = "$\\mathit{df}$", class = c("tiny_labelled", "character"))
, df.residual = structure("22", label = "$\\mathit{df}_{\\mathrm{res}}$", class = c("tiny_labelled", "character"))
, mse = structure("31.23", label = "$\\mathit{MSE}$", class = c("tiny_labelled", "character"))
, p.value = structure(".022", label = "$p$", class = c("tiny_labelled", "character"))
)
, row.names = 1L
, class = c("apa_results_table", "data.frame")
)
)
}
)
test_that(
"Warn if observed factors do not match"
, {
expect_warning(
apa_print(afex::aov_4(yield~(N*P|block), data = npk, observed = "N"), observed = "P")
, regexp = "In your call to apa_print(), you specified the model terms \"P\" as observed, whereas in your call to afex::aov_car(), you specified the model terms \"N\" as observed. Make sure that this is what you want."
, fixed = TRUE
)
}
)
test_that(
"Ensure proper sorting of terms"
, {
load("data/mixed_data.rdata")
unsorted_aov <- afex::aov_4(formula = Recall ~ Gender * Dosage * (Task|Subject), data = mixed_data, fun_aggregate = mean)
apa_out <- apa_print(unsorted_aov)
expect_equal(
unlabel(gsub(apa_out$table$term, pattern = " $\\times$ ", replacement = "_", fixed = TRUE))
, names(apa_out$estimate)
)
}
)
test_that(
"Suppress MSE if requested"
, {
npk$id <- seq_len(nrow(npk))
out <- apa_print(
afex::aov_4(
formula = yield ~ N + (1 | id)
, data = npk
, fun_aggregate = mean
)
, estimate = "ges"
, mse = FALSE
)
expect_identical(
colnames(out$table)
, c("term", "estimate", "statistic", "df", "df.residual", "p.value")
)
out <- apa_print(
afex::aov_4(
formula = yield ~ (N | block)
, data = npk
, fun_aggregate = mean
)
, estimate = "ges"
, mse = FALSE
)
expect_identical(
colnames(out$table)
, c("term", "estimate", "statistic", "df", "df.residual", "p.value")
)
}
)
test_that(
"Analysis of deviance from car"
, {
fit <- bwt.mu <- nnet::multinom(low ~ ., MASS::birthwt)
car_out <- papaja::apa_print(car::Anova(fit))
expect_apa_results(
car_out
, labels = list(
term = "Term"
, statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
# Example 1: a proportional odds model fitted to pneumo.
set.seed(1)
pneumo <- transform(VGAM::pneumo, let = log(exposure.time), x3 = runif(8))
fit1 <- VGAM::vglm(cbind(normal, mild, severe) ~ let , VGAM::propodds, pneumo)
fit2 <- VGAM::vglm(cbind(normal, mild, severe) ~ let + x3, VGAM::propodds, pneumo)
fit3 <- VGAM::vglm(cbind(normal, mild, severe) ~ let + x3, VGAM::cumulative, pneumo)
car_out <- apa_print(car::Anova(fit1, type = 3))
expect_apa_results(
car_out
, labels = list(
term = "Term"
, statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
}
)
test_that(
"Analysis of deviance from the stats and car packages"
, {
# From stats::glm() examples section:
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
data.frame(treatment, outcome, counts) # showing data
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
chisq_out <- apa_print(anova(glm.D93, test = "Chisq"))
cp_out <- apa_print(anova(glm.D93, test = "Cp"))
lrt_out <- apa_print(anova(glm.D93, test = "LRT"))
rao_out <- apa_print(anova(glm.D93, test = "Rao"))
expect_identical(
chisq_out$full_result
, list(
outcome = "$\\chi^2(2) = 5.45$, $p = .065$"
, treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
)
)
expect_identical(
cp_out$full_result
, list(
outcome = "$C_p = 11.13$"
, treatment = "$C_p = 15.13$"
)
)
expect_identical(
lrt_out
, chisq_out
)
expect_identical(
rao_out$full_result
, list(
outcome = "$\\mathit{RS}(2) = 5.56$, $p = .062$"
, treatment = "$\\mathit{RS}(2) = 0.00$, $p > .999$"
)
)
car_lr_out <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "LR"))
car_wald_out <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "Wald"))
car_f_out <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "F"))
expect_identical(
car_lr_out$full_result
, list(
outcome = "$\\chi^2(2) = 5.45$, $p = .065$"
, treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
)
)
expect_identical(
car_wald_out$full_result
, list(
Intercept = "$\\chi^2(1) = 317.37$, $p < .001$"
, outcome = "$\\chi^2(2) = 5.49$, $p = .064$"
, treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
)
)
expect_identical(
car_f_out$full_result
, list(
outcome = "$F(2, 4) = 2.11$, $p = .237$"
, treatment = "$F(2, 4) = 0.00$, $p > .999$"
)
)
}
)
# restore previous options
options(op)
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context("apa_print() for ANOVA")
# Data and examples from http://personality-project.org/r/r.guide.html#anova
# Use our own effect-size function for these tests
# Custom effect sizes via the 'effectsize' package are tested elsewhere
op <- options(papaja.estimate_anova = "ges", papaja.mse = TRUE)
test_that(
"One-way between ANOVA"
, {
load("data/ow_data.rdata")
ow_aov <- aov(Alertness ~ Dosage, data = ow_data)
ow_aov_output <- apa_print(ow_aov)
expect_apa_results(
ow_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(ow_aov_output$stat), "Dosage")
expect_identical(ow_aov_output$stat$Dosage, "$F(2, 15) = 8.79$, $\\mathit{MSE} = 24.25$, $p = .003$")
# est
expect_identical(names(ow_aov_output$est), "Dosage")
expect_identical(ow_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .540$")
# full
expect_identical(names(ow_aov_output$full), "Dosage")
expect_identical(ow_aov_output$full$Dosage, "$F(2, 15) = 8.79$, $\\mathit{MSE} = 24.25$, $p = .003$, $\\hat{\\eta}^2_G = .540$")
# table
expect_identical(nrow(ow_aov_output$table), 1L)
# Other classes
ow_aov_summary_output <- apa_print(summary(ow_aov))
expect_identical(ow_aov_summary_output, ow_aov_output)
ow_aov_Anova_output <- apa_print(car::Anova(ow_aov))
expect_identical(ow_aov_Anova_output, ow_aov_output)
ow_afex_data <- cbind(id = 1:nrow(ow_data), ow_data)
ow_afex_aov <- afex::aov_ez(
data = ow_afex_data
, id = "id"
, dv = "Alertness"
, between = "Dosage"
)
ow_afex_aov_output <- apa_print(ow_afex_aov$aov)
expect_identical(ow_afex_aov_output, ow_aov_output)
ow_afex_aov_output <- apa_print(ow_afex_aov$Anova)
expect_identical(ow_afex_aov_output, ow_aov_output)
ow_afex_aov_output <- apa_print(ow_afex_aov)
expect_identical(ow_afex_aov_output, ow_aov_output)
# With intercept
ow_afex_aov_output <- apa_print(ow_afex_aov$Anova, intercept = TRUE)
expect_apa_results(
ow_afex_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(ow_afex_aov_output$stat), c("Intercept", "Dosage"))
expect_is(ow_afex_aov_output$stat$Intercept, "character")
expect_identical(ow_afex_aov_output$stat$Intercept, "$F(1, 15) = 487.23$, $\\mathit{MSE} = 24.25$, $p < .001$")
# est
expect_identical(names(ow_afex_aov_output$est), c("Intercept", "Dosage"))
expect_is(ow_afex_aov_output$est$Intercept, "character")
expect_identical(ow_afex_aov_output$est$Intercept, "$\\hat{\\eta}^2_G = .970$")
# full
expect_identical(names(ow_afex_aov_output$full), c("Intercept", "Dosage"))
expect_is(ow_afex_aov_output$full$Intercept, "character")
expect_identical(ow_afex_aov_output$full$Intercept, "$F(1, 15) = 487.23$, $\\mathit{MSE} = 24.25$, $p < .001$, $\\hat{\\eta}^2_G = .970$")
# table
expect_identical(nrow(ow_afex_aov_output$table), 2L)
expect_identical(
ow_afex_aov_output$table$term
, structure(
c("Intercept", "Dosage")
, class = c("tiny_labelled", "character")
, label = "Effect"
)
)
ow_afex_aov_output2 <- apa_print(ow_afex_aov, intercept = TRUE)
expect_identical(ow_afex_aov_output2, ow_afex_aov_output)
# Other effect sizes
ow_aov_output <- apa_print(ow_aov, es = "pes")
expect_identical(ow_aov_output$est$Dosage, "$\\hat{\\eta}^2_p = .540$")
ow_aov_output <- apa_print(ow_aov, es = c("ges"))
expect_apa_results(ow_aov_output)
expect_identical(ow_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .540$")
}
)
test_that(
"Two-way between ANOVA"
, {
load("data/tw_data.rdata")
tw_aov <- aov(Alertness ~ Gender * Dosage, tw_data)
tw_aov_output <- apa_print(tw_aov)
expect_apa_results(
tw_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(tw_aov_output$stat), c("Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_aov_output$stat$Gender, "$F(1, 12) = 2.95$, $\\mathit{MSE} = 25.94$, $p = .111$")
expect_identical(tw_aov_output$stat$Dosage, "$F(1, 12) = 0.20$, $\\mathit{MSE} = 25.94$, $p = .666$")
expect_identical(tw_aov_output$stat$Gender_Dosage, "$F(1, 12) = 0.00$, $\\mathit{MSE} = 25.94$, $p = .962$")
# est
expect_identical(names(tw_aov_output$est), c("Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_aov_output$est$Gender, "$\\hat{\\eta}^2_G = .197$")
expect_identical(tw_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .016$")
expect_identical(tw_aov_output$est$Gender_Dosage, "$\\hat{\\eta}^2_G = .000$")
# full
expect_identical(names(tw_aov_output$full), c("Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_aov_output$full$Gender, "$F(1, 12) = 2.95$, $\\mathit{MSE} = 25.94$, $p = .111$, $\\hat{\\eta}^2_G = .197$")
expect_identical(tw_aov_output$full$Dosage, "$F(1, 12) = 0.20$, $\\mathit{MSE} = 25.94$, $p = .666$, $\\hat{\\eta}^2_G = .016$")
expect_identical(tw_aov_output$full$Gender_Dosage, "$F(1, 12) = 0.00$, $\\mathit{MSE} = 25.94$, $p = .962$, $\\hat{\\eta}^2_G = .000$")
# table
expect_identical(nrow(tw_aov_output$table), 3L)
expect_identical(
tw_aov_output$table$term
, expected = structure(
c("Gender", "Dosage", "Gender $\\times$ Dosage")
, label = "Effect"
, class = c("tiny_labelled", "character")
)
)
# Other classes
tw_aov_Anova_output <- apa_print(car::Anova(tw_aov))
expect_identical(tw_aov_Anova_output, tw_aov_output)
tw_aov_summary_output <- apa_print(summary(tw_aov))
expect_identical(tw_aov_summary_output, tw_aov_output)
tw_afex_data <- cbind(id = 1:nrow(tw_data), tw_data)
tw_afex_aov <- afex::aov_ez(
data = tw_afex_data
, id = "id"
, dv = "Alertness"
, between = c("Gender", "Dosage")
)
tw_afex_aov_output <- apa_print(tw_afex_aov$aov)
expect_identical(tw_afex_aov_output, tw_aov_output)
# With intercept
tw_afex_aov_output <- apa_print(tw_afex_aov$Anova, intercept = TRUE)
expect_apa_results(
tw_afex_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
## stat
expect_identical(names(tw_afex_aov_output$stat), c("Intercept", "Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_afex_aov_output$stat$Intercept, "$F(1, 12) = 121.99$, $\\mathit{MSE} = 25.94$, $p < .001$")
## est
expect_identical(names(tw_afex_aov_output$est), c("Intercept", "Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_afex_aov_output$est$Intercept, "$\\hat{\\eta}^2_G = .910$")
## full
expect_identical(names(tw_afex_aov_output$full), c("Intercept", "Gender", "Dosage", "Gender_Dosage"))
expect_identical(tw_afex_aov_output$full$Intercept, "$F(1, 12) = 121.99$, $\\mathit{MSE} = 25.94$, $p < .001$, $\\hat{\\eta}^2_G = .910$")
## table
expect_identical(nrow(tw_afex_aov_output$table), 4L)
expect_identical(
object = tw_afex_aov_output$table$term
, expected = structure(
c("Intercept", "Gender", "Dosage", "Gender $\\times$ Dosage")
, label = "Effect"
, class = c("tiny_labelled", "character")
)
)
tw_afex_aov_output2 <- apa_print(tw_afex_aov, intercept = TRUE)
expect_identical(tw_afex_aov_output2, tw_afex_aov_output)
# Observed effects
tw_rm_aov_output <- apa_print(tw_aov, observed = "Gender")
expect_identical(tw_rm_aov_output$est$Gender, "$\\hat{\\eta}^2_G = .197$")
expect_identical(tw_rm_aov_output$est$Dosage, "$\\hat{\\eta}^2_G = .013$")
expect_identical(tw_rm_aov_output$est$Gender_Dosage, "$\\hat{\\eta}^2_G = .000$")
}
)
test_that(
"One-way repeated-measures ANOVA"
, {
options(papaja.estimate_anova = "ges")
load("data/rm_data.rdata")
rm_aov <- aov(Recall ~ Valence + Error(Subject/Valence), rm_data)
rm_aov_output <- apa_print(rm_aov)
expect_apa_results(
rm_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(rm_aov_output$stat), "Valence")
expect_identical(rm_aov_output$stat$Valence, "$F(2, 8) = 189.11$, $\\mathit{MSE} = 5.37$, $p < .001$")
# est
expect_identical(names(rm_aov_output$est), "Valence")
expect_identical(rm_aov_output$est$Valence, "$\\hat{\\eta}^2_G = .932$")
# full
expect_identical(names(rm_aov_output$full), "Valence")
expect_identical(rm_aov_output$full$Valence, "$F(2, 8) = 189.11$, $\\mathit{MSE} = 5.37$, $p < .001$, $\\hat{\\eta}^2_G = .932$")
# Other classes
rm_aov_summary_output <- apa_print(summary(rm_aov))
expect_identical(rm_aov_summary_output, rm_aov_output)
rm_afex_aov <- afex::aov_ez(
data = rm_data
, id = "Subject"
, dv = "Recall"
, within = "Valence"
)
rm_afex_aov_output <- apa_print(rm_afex_aov$aov)
expect_identical(rm_afex_aov_output, rm_aov_output)
rm_afex_aov_output <- apa_print(rm_afex_aov$Anova, correction = "none", intercept = FALSE)
expect_identical(rm_afex_aov_output, rm_aov_output)
rm_afex_aov_output <- apa_print(rm_afex_aov, correction = "none", intercept = FALSE)
expect_identical(rm_afex_aov_output, rm_aov_output)
# DF corrections
rm_afex_anova.mlm_output <- apa_print(rm_afex_aov$Anova, correction = "GG")
expect_apa_results(
rm_afex_anova.mlm_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}^{\\mathrm{GG}}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}^{\\mathrm{GG}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
expect_identical(rm_afex_anova.mlm_output$full$Valence, "$F(1.15, 4.60) = 189.11$, $\\mathit{MSE} = 9.34$, $p < .001$, $\\hat{\\eta}^2_G = .932$")
rm_afex_anova.mlm_output <- apa_print(rm_afex_aov$Anova, correction = "HF")
expect_apa_results(
rm_afex_anova.mlm_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}^{\\mathrm{HF}}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}^{\\mathrm{HF}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
expect_identical(rm_afex_anova.mlm_output$full$Valence, "$F(1.32, 5.26) = 189.11$, $\\mathit{MSE} = 8.16$, $p < .001$, $\\hat{\\eta}^2_G = .932$")
rm_afex_anova.mlm_output <- apa_print(rm_afex_aov$Anova, correction = "none")
expect_identical(rm_afex_anova.mlm_output$full$Valence, rm_aov_output$full$Valence)
}
)
test_that(
"Two-way repeated-measures ANOVA"
, {
load("data/tw_rm_data.rdata")
tw_rm_aov <- aov(Recall~ (Task * Valence) + Error(Subject/(Task * Valence)), tw_rm_data)
tw_rm_aov_output <- apa_print(tw_rm_aov)
expect_apa_results(
tw_rm_aov_output
, labels = list(
term = "Effect"
, estimate = "$\\hat{\\eta}^2_G$"
, statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, mse = "$\\mathit{MSE}$"
, p.value = "$p$"
)
)
# stat
expect_identical(names(tw_rm_aov_output$stat), c("Task", "Valence", "Task_Valence"))
expect_identical(tw_rm_aov_output$stat$Task, "$F(1, 4) = 7.35$, $\\mathit{MSE} = 4.08$, $p = .054$")
expect_identical(tw_rm_aov_output$stat$Valence, "$F(2, 8) = 1.46$, $\\mathit{MSE} = 3.36$, $p = .288$")
expect_identical(tw_rm_aov_output$stat$Task_Valence, "$F(2, 8) = 0.29$, $\\mathit{MSE} = 2.41$, $p = .755$")
# est
expect_identical(names(tw_rm_aov_output$est), c("Task", "Valence", "Task_Valence"))
expect_identical(tw_rm_aov_output$est$Task, "$\\hat{\\eta}^2_G = .068$")
expect_identical(tw_rm_aov_output$est$Valence, "$\\hat{\\eta}^2_G = .023$")
expect_identical(tw_rm_aov_output$est$Task_Valence, "$\\hat{\\eta}^2_G = .003$")
# full
expect_identical(names(tw_rm_aov_output$full), c("Task", "Valence", "Task_Valence"))
expect_identical(tw_rm_aov_output$full$Task, "$F(1, 4) = 7.35$, $\\mathit{MSE} = 4.08$, $p = .054$, $\\hat{\\eta}^2_G = .068$")
expect_identical(tw_rm_aov_output$full$Valence, "$F(2, 8) = 1.46$, $\\mathit{MSE} = 3.36$, $p = .288$, $\\hat{\\eta}^2_G = .023$")
expect_identical(tw_rm_aov_output$full$Task_Valence, "$F(2, 8) = 0.29$, $\\mathit{MSE} = 2.41$, $p = .755$, $\\hat{\\eta}^2_G = .003$")
# Other classes
tw_rm_aov_summary_output <- apa_print(summary(tw_rm_aov))
expect_identical(tw_rm_aov_summary_output, tw_rm_aov_output)
tw_rm_afex_aov <- suppressWarnings(afex::aov_ez(
data = tw_rm_data
, id = "Subject"
, dv = "Recall"
, within = c("Task", "Valence")
))
tw_rm_afex_aov_output <- apa_print(tw_rm_afex_aov$aov)
expect_identical(tw_rm_afex_aov_output, tw_rm_aov_output)
tw_rm_afex_aov_output <- apa_print(tw_rm_afex_aov$Anova, correction = "none")
expect_identical(tw_rm_afex_aov_output, tw_rm_aov_output)
tw_rm_afex_aov_output <- suppressWarnings(apa_print(tw_rm_afex_aov, correction = "none"))
expect_identical(tw_rm_afex_aov_output, tw_rm_aov_output)
# Observed
tw_rm_aov_output <- apa_print(tw_rm_aov, observed = "Task")
expect_identical(tw_rm_aov_output$est$Task, "$\\hat{\\eta}^2_G = .068$")
expect_identical(tw_rm_aov_output$est$Valence, "$\\hat{\\eta}^2_G = .022$")
expect_identical(tw_rm_aov_output$est$Task_Valence, "$\\hat{\\eta}^2_G = .003$")
}
)
test_that(
"Levene test"
, {
load("data/ow_data.rdata")
levene_test <- car::leveneTest(Alertness ~ Dosage, data = ow_data)
levene_test_output <- apa_print(levene_test)
expect_apa_results(
levene_test_output
, labels = list(
statistic = "$F$"
, df = "$\\mathit{df}$"
, df.residual = "$\\mathit{df}_{\\mathrm{res}}$"
, p.value = "$p$"
)
)
expect_identical(levene_test_output$stat, "$F(2, 15) = 4.17$, $p = .036$")
}
)
test_that(
"anova(lm(...))"
, {
model <- anova(lm(yield ~ N, data = npk))
apa_out <- apa_print(model)
expect_identical(
apa_out$table
, expected = structure(
list(
term = structure("N", label = "Effect", class = c("tiny_labelled", "character"))
, estimate = structure(".216", label = "$\\hat{\\eta}^2_G$", class = c("tiny_labelled","character"))
, statistic = structure("6.06", label = "$F$", class = c("tiny_labelled", "character"))
, df = structure("1", label = "$\\mathit{df}$", class = c("tiny_labelled", "character"))
, df.residual = structure("22", label = "$\\mathit{df}_{\\mathrm{res}}$", class = c("tiny_labelled", "character"))
, mse = structure("31.23", label = "$\\mathit{MSE}$", class = c("tiny_labelled", "character"))
, p.value = structure(".022", label = "$p$", class = c("tiny_labelled", "character"))
)
, row.names = 1L
, class = c("apa_results_table", "data.frame")
)
)
}
)
test_that(
"Warn if observed factors do not match"
, {
expect_warning(
apa_print(afex::aov_4(yield~(N*P|block), data = npk, observed = "N"), observed = "P")
, regexp = "In your call to apa_print(), you specified the model terms \"P\" as observed, whereas in your call to afex::aov_car(), you specified the model terms \"N\" as observed. Make sure that this is what you want."
, fixed = TRUE
)
}
)
test_that(
"Ensure proper sorting of terms"
, {
load("data/mixed_data.rdata")
unsorted_aov <- afex::aov_4(formula = Recall ~ Gender * Dosage * (Task|Subject), data = mixed_data, fun_aggregate = mean)
apa_out <- apa_print(unsorted_aov)
expect_equal(
unlabel(gsub(apa_out$table$term, pattern = " $\\times$ ", replacement = "_", fixed = TRUE))
, names(apa_out$estimate)
)
}
)
test_that(
"Suppress MSE if requested"
, {
npk$id <- seq_len(nrow(npk))
out <- apa_print(
afex::aov_4(
formula = yield ~ N + (1 | id)
, data = npk
, fun_aggregate = mean
)
, estimate = "ges"
, mse = FALSE
)
expect_identical(
colnames(out$table)
, c("term", "estimate", "statistic", "df", "df.residual", "p.value")
)
out <- apa_print(
afex::aov_4(
formula = yield ~ (N | block)
, data = npk
, fun_aggregate = mean
)
, estimate = "ges"
, mse = FALSE
)
expect_identical(
colnames(out$table)
, c("term", "estimate", "statistic", "df", "df.residual", "p.value")
)
}
)
test_that(
"Analysis of deviance from car"
, {
fit <- bwt.mu <- nnet::multinom(low ~ ., MASS::birthwt)
car_out <- papaja::apa_print(car::Anova(fit))
expect_apa_results(
car_out
, labels = list(
term = "Term"
, statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
# Example 1: a proportional odds model fitted to pneumo.
set.seed(1)
pneumo <- transform(VGAM::pneumo, let = log(exposure.time), x3 = runif(8))
fit1 <- VGAM::vglm(cbind(normal, mild, severe) ~ let , VGAM::propodds, pneumo)
fit2 <- VGAM::vglm(cbind(normal, mild, severe) ~ let + x3, VGAM::propodds, pneumo)
fit3 <- VGAM::vglm(cbind(normal, mild, severe) ~ let + x3, VGAM::cumulative, pneumo)
car_out <- apa_print(car::Anova(fit1, type = 3))
expect_apa_results(
car_out
, labels = list(
term = "Term"
, statistic = "$\\chi^2$"
, df = "$\\mathit{df}$"
, p.value = "$p$"
)
)
}
)
test_that(
"Analysis of deviance from the stats and car packages"
, {
# From stats::glm() examples section:
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
data.frame(treatment, outcome, counts) # showing data
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
chisq_out <- apa_print(anova(glm.D93, test = "Chisq"))
cp_out <- apa_print(anova(glm.D93, test = "Cp"))
lrt_out <- apa_print(anova(glm.D93, test = "LRT"))
rao_out <- apa_print(anova(glm.D93, test = "Rao"))
expect_identical(
chisq_out$full_result
, list(
outcome = "$\\chi^2(2) = 5.45$, $p = .065$"
, treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
)
)
expect_identical(
cp_out$full_result
, list(
outcome = "$C_p = 11.13$"
, treatment = "$C_p = 15.13$"
)
)
expect_identical(
lrt_out
, chisq_out
)
expect_identical(
rao_out$full_result
, list(
outcome = "$\\mathit{RS}(2) = 5.56$, $p = .062$"
, treatment = "$\\mathit{RS}(2) = 0.00$, $p > .999$"
)
)
car_lr_out <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "LR"))
car_wald_out <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "Wald"))
car_f_out <- apa_print(car::Anova(glm.D93, type = 3, test.statistic = "F"))
expect_identical(
car_lr_out$full_result
, list(
outcome = "$\\chi^2(2) = 5.45$, $p = .065$"
, treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
)
)
expect_identical(
car_wald_out$full_result
, list(
Intercept = "$\\chi^2(1) = 317.37$, $p < .001$"
, outcome = "$\\chi^2(2) = 5.49$, $p = .064$"
, treatment = "$\\chi^2(2) = 0.00$, $p > .999$"
)
)
expect_identical(
car_f_out$full_result
, list(
outcome = "$F(2, 4) = 2.11$, $p = .237$"
, treatment = "$F(2, 4) = 0.00$, $p > .999$"
)
)
}
)
# restore previous options
options(op)
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