tests/testthat/test_apa_print_BFBayesFactor.R
In crsh/papaja: Prepare American Psychological Association Journal Articles with R Markdown
context("apa_print.BFBayesFactor()")
test_that(
"Parse interval hypotheses"
, {
alt <- data.frame(
1:3
, row.names = c(
"Alt., r=0.707 0<d<Inf"
, "Alt., r=0.707 !(0<d<Inf)"
, "Alt., r=0.707 !(5<d<10)"
)
)
alternative_intervals <- add_alternative(
alt
, range = c(-Inf, Inf)
)[, "alternative", drop = FALSE]
expect_identical(
alternative_intervals
, data.frame(
alternative = c(
"$[0.00, \\infty]$"
, "$[-\\infty, 0.00]$"
, "$[-\\infty, 5.00]~\\cup~[10.00, \\infty]$"
)
)
)
alt <- data.frame(
1:2
, row.names = c(
"Alt., r=0.333 0<rho<0.5"
, "Alt., r=0.333 !(0<rho<0.5)"
)
)
alternative_intervals <- add_alternative(
alt
, range = c(-1, 1)
)[, "alternative", drop = FALSE]
expect_identical(
alternative_intervals
, data.frame(
alternative = c(
"$[0.00, 0.50]$"
, "$[-1.00, 0.00]~\\cup~[0.50, 1.00]$"
)
)
)
}
)
test_that(
"ttestBF(): One sample"
, {
set.seed(123)
ttest <- BayesFactor::ttestBF(
x = sleep$extra[sleep$group == 1] - sleep$extra[sleep$group == 2]
)
ttest_output <- apa_print(
ttest
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
ttest_output
, labels = list(
estimate = "$M$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(
ttest_output$stat
, "$\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
expect_identical(
ttest_output$esti
, "$M = -1.43$, 95\\% HDI $[-2.33, -0.54]$"
)
expect_identical(
ttest_output$full
, "$M = -1.43$, 95\\% HDI $[-2.33, -0.54]$, $\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
set.seed(123)
ttest_paired <- BayesFactor::ttestBF(
x = sleep$extra[sleep$group == 1]
, y = sleep$extra[sleep$group == 2]
, paired = TRUE
)
ttest_paired_output <- apa_print(
ttest_paired
, est_name = "\\Delta M"
, central_tendency = median
, iterations = 10000
)
expect_equivalent(
ttest_paired_output$stat
, "$\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
expect_identical(
ttest_paired_output$esti
, "$\\Delta M = -1.43$, 95\\% HDI $[-2.33, -0.54]$"
)
expect_identical(
ttest_paired_output$full
, "$\\Delta M = -1.43$, 95\\% HDI $[-2.33, -0.54]$, $\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
# Custom interval and MCMC error
set.seed(123)
ttest_interval <- apa_print(
ttest_paired
, iterations = 1000
, interval = function(x) quantile(x, probs = c(0.025, 0.975))
, interval_type = "CrI"
, mcmc_error = TRUE
)
expect_apa_results(
ttest_interval
, labels = list(
estimate = "$M$"
, conf.int = "95\\% CrI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
, mcmc.error = "$\\pm\\%$"
)
)
expect_identical(
ttest_interval$full
, "$M = -1.43$, 95\\% CrI $[-2.31, -0.44]$, $\\mathrm{BF}_{\\textrm{10}} = 17.26 \\pm 0.00\\%$"
)
# bf_r1
set.seed(123)
ttest_r1 <- apa_print(
ttest_paired
, bf_r1 = 3
)
expect_equivalent(
ttest_r1$stat
, "$\\mathrm{BF}_{\\textrm{10}} = 51.78$"
)
set.seed(123)
ttest_1r <- apa_print(
ttest_paired
, bf_1r = 3
)
expect_identical(ttest_r1, ttest_1r)
}
)
test_that(
"ttestBF(): Independent samples"
, {
set.seed(123)
ttest <- BayesFactor::ttestBF(x = sleep$extra[sleep$group == 1], y = sleep$extra[sleep$group == 2])
ttest_output <- apa_print(
ttest
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
ttest_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(ttest_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 1.27$")
expect_identical(ttest_output$esti, "$\\Delta M = -1.13$, 95\\% HDI $[-2.76, 0.46]$")
expect_identical(ttest_output$full, "$\\Delta M = -1.13$, 95\\% HDI $[-2.76, 0.46]$, $\\mathrm{BF}_{\\textrm{10}} = 1.27$")
# Formula method ----
set.seed(123L)
ttest_form <- BayesFactor::ttestBF(formula = extra ~ group, data = sleep)
ttest_form_output <- apa_print(
ttest_form
, central_tendency = median
, iterations = 10000
)
expect_identical(ttest_output, ttest_form_output)
set.seed(123)
ttest_onesided <- BayesFactor::ttestBF(x = sleep$extra[sleep$group == 1], y = sleep$extra[sleep$group == 2], nullInterval = c(0, Inf))
ttest_onesided_output <- apa_print(
ttest_onesided
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
ttest_onesided_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "95\\% HDI"
, alternative = "$\\mathcal{H}_1$"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_apa_term(
ttest_onesided_output
, term = "interval"
, estimate = "$\\Delta M = 0.26$, 95\\% HDI $[0.00, 1.00]$"
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 0.17$"
)
expect_apa_term(
ttest_onesided_output
, term = "inverse_interval"
, estimate = "$\\Delta M = 0.26$, 95\\% HDI $[0.00, 1.00]$"
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 2.36$"
)
ttest_output <- apa_print(
ttest_onesided
, mcmc_error = FALSE
, central_tendency = median
, iterations = 10000
)
expect_null(ttest_output$table$mcmc.error)
}
)
test_that(
"correlationBF()"
, {
set.seed(123)
corr <- BayesFactor::correlationBF(
x = iris$Sepal.Width
, y = iris$Sepal.Length
)
corr_output <- apa_print(
corr
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
corr_output
, labels = list(
estimate = "$r$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(corr_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 0.51$")
expect_identical(corr_output$esti, "$r = -0.11$, 95\\% HDI $[-0.27, 0.04]$")
expect_identical(corr_output$full, "$r = -0.11$, 95\\% HDI $[-0.27, 0.04]$, $\\mathrm{BF}_{\\textrm{10}} = 0.51$")
}
)
test_that(
"proportionBF()"
, {
set.seed(123)
prop <- BayesFactor::proportionBF(y = 15, N = 25, p = .5)
prop_output <- apa_print(
prop
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
prop_output
, labels = list(
estimate = "$\\hat\\pi$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(prop_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 0.66$")
expect_identical(prop_output$esti, "$\\hat\\pi = .58$, 95\\% HDI $[.40, .74]$")
expect_identical(prop_output$full, "$\\hat\\pi = .58$, 95\\% HDI $[.40, .74]$, $\\mathrm{BF}_{\\textrm{10}} = 0.66$")
}
)
test_that(
"contingencyTableBF()"
, {
data(raceDolls, package = "BayesFactor")
set.seed(123)
cont <- BayesFactor::contingencyTableBF(raceDolls, sampleType = "indepMulti", fixedMargin = "cols")
cont_output <- apa_print(
cont
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
cont_output
, labels = list(
statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_identical(cont_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 1.81$")
expect_identical(cont_output$full, cont_output$stat)
}
)
test_that(
"anovaBF()"
, {
data(puzzles, package = "BayesFactor")
set.seed(123)
anova_bf_main = BayesFactor::anovaBF(
RT ~ shape*color + ID
, data = puzzles
, whichRandom = "ID"
, whichModels = "withmain"
, progress = FALSE
)
anova_bf_main_output <- apa_print(anova_bf_main)
expect_apa_results(
anova_bf_main_output
, labels = list(
model = "Model"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
, mcmc.error = "$\\pm\\%$"
)
)
expect_apa_term(
anova_bf_main_output
, term = "shape_ID"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 2.84 \\pm 1.12\\%$"
)
set.seed(123)
anova_bf_top = BayesFactor::anovaBF(
RT ~ shape*color + ID
, data = puzzles
, whichRandom = "ID"
, whichModels = "top"
, progress = FALSE
)
anova_bf_top_output <- apa_print(anova_bf_top)
expect_apa_results(
anova_bf_top_output
, labels = list(
term = "Term"
, statistic = "$\\mathrm{BF}_{\\textrm{01}}$"
)
)
expect_apa_term(
anova_bf_top_output
, term = "color_shape"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.65$"
)
anova_bf_top_output <- apa_print(
anova_bf_top
, scientific_threshold = c(min = 0.99, max = 1.1)
)
expect_apa_term(
anova_bf_top_output
, term = "color_shape"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.65$"
)
expect_apa_term(
anova_bf_top_output
, term = "color"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.33 \\times 10^{-1}$"
)
anova_bf_top_output <- apa_print(anova_bf_top, log = TRUE)
expect_apa_results(
anova_bf_top_output
, labels = list(
term = "Term"
, statistic = "$\\log \\mathrm{BF}_{\\textrm{01}}$"
)
)
expect_apa_term(
anova_bf_top_output
, term = "color"
, estimate = NULL
, statistic = "$\\log \\mathrm{BF}_{\\textrm{01}} = -1.45$"
)
}
)
test_that(
"regressionBF()"
, {
data(attitude)
set.seed(123)
regression_bf_all <- BayesFactor::regressionBF(
rating ~ complaints + privileges + learning + raises
, data = attitude
, whichModels = "all"
, progress = FALSE
)
regression_bf_all_output <- apa_print(regression_bf_all)
expect_apa_results(
regression_bf_all_output
, labels = list(
model = "Model"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_apa_term(
regression_bf_all_output
, term = "privileges_raises"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 25.90$"
)
regression_bf_top <- BayesFactor::regressionBF(
rating ~ complaints + privileges + learning + raises
, data = attitude
, whichModels = "top"
, progress = FALSE
)
regression_bf_top_output <- apa_print(regression_bf_top)
expect_apa_results(
regression_bf_top_output
, labels = list(
term = "Term"
, statistic = "$\\mathrm{BF}_{\\textrm{01}}$"
)
)
expect_apa_term(
regression_bf_top_output
, term = "complaints"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.85 \\times 10^{-3}$"
)
regression_bf_top_output <- apa_print(regression_bf_top, reciprocal = TRUE)
expect_apa_term(
regression_bf_top_output
, term = "complaints"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 351.26$"
)
regression_bf_top_output <- apa_print(
regression_bf_top
, log = TRUE
, reciprocal = TRUE
)
expect_apa_term(
regression_bf_top_output
, term = "complaints"
, estimate = NULL
, statistic = "$\\log \\mathrm{BF}_{\\textrm{10}} = 5.86$"
)
}
)
test_that(
"lmBF()"
, {
set.seed(1298)
data(puzzles, package = "BayesFactor")
## Bayes factor of full model against null
bf_full <- BayesFactor::lmBF(
RT ~ shape + color + shape:color + ID + ID:shape + ID:color
, data = puzzles
, whichRandom = c("ID", "ID:shape", "ID:color")
)
expect_identical(
apa_print(bf_full)$statistic
, "$\\mathrm{BF}_{\\textrm{10}} = 3,421.93$"
)
# bf_shape <- lmBF(
# RT ~ color + shape:color + ID + ID:shape + ID:color
# , data = puzzles
# , whichRandom = c("ID", "ID:shape", "ID:color")
# )
# bf_color <- lmBF(
# RT ~ shape + shape:color + ID + ID:shape + ID:color
# , data = puzzles
# , whichRandom = c("ID", "ID:shape", "ID:color")
# )
# bf <- bf_full / c(bf_shape, bf_color)
# bf_res <- apa_print(bf)
}
)
test_that(
"generalTestBF()"
, {
set.seed(1298)
data(puzzles, package = "BayesFactor")
## Bayes factor of full model against null
bf <- BayesFactor::generalTestBF(
RT ~ shape * color + ID
, data = puzzles
, whichRandom = "ID"
, progress = FALSE
)
bf_res <- apa_print(bf)
expect_apa_term(
bf_res
, term = "ID"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 1.12 \\times 10^{5} \\pm 0.00\\%$"
)
}
)
crsh/papaja documentation built on Feb. 21, 2024, 6:11 p.m.
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context("apa_print.BFBayesFactor()")
test_that(
"Parse interval hypotheses"
, {
alt <- data.frame(
1:3
, row.names = c(
"Alt., r=0.707 0<d<Inf"
, "Alt., r=0.707 !(0<d<Inf)"
, "Alt., r=0.707 !(5<d<10)"
)
)
alternative_intervals <- add_alternative(
alt
, range = c(-Inf, Inf)
)[, "alternative", drop = FALSE]
expect_identical(
alternative_intervals
, data.frame(
alternative = c(
"$[0.00, \\infty]$"
, "$[-\\infty, 0.00]$"
, "$[-\\infty, 5.00]~\\cup~[10.00, \\infty]$"
)
)
)
alt <- data.frame(
1:2
, row.names = c(
"Alt., r=0.333 0<rho<0.5"
, "Alt., r=0.333 !(0<rho<0.5)"
)
)
alternative_intervals <- add_alternative(
alt
, range = c(-1, 1)
)[, "alternative", drop = FALSE]
expect_identical(
alternative_intervals
, data.frame(
alternative = c(
"$[0.00, 0.50]$"
, "$[-1.00, 0.00]~\\cup~[0.50, 1.00]$"
)
)
)
}
)
test_that(
"ttestBF(): One sample"
, {
set.seed(123)
ttest <- BayesFactor::ttestBF(
x = sleep$extra[sleep$group == 1] - sleep$extra[sleep$group == 2]
)
ttest_output <- apa_print(
ttest
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
ttest_output
, labels = list(
estimate = "$M$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(
ttest_output$stat
, "$\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
expect_identical(
ttest_output$esti
, "$M = -1.43$, 95\\% HDI $[-2.33, -0.54]$"
)
expect_identical(
ttest_output$full
, "$M = -1.43$, 95\\% HDI $[-2.33, -0.54]$, $\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
set.seed(123)
ttest_paired <- BayesFactor::ttestBF(
x = sleep$extra[sleep$group == 1]
, y = sleep$extra[sleep$group == 2]
, paired = TRUE
)
ttest_paired_output <- apa_print(
ttest_paired
, est_name = "\\Delta M"
, central_tendency = median
, iterations = 10000
)
expect_equivalent(
ttest_paired_output$stat
, "$\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
expect_identical(
ttest_paired_output$esti
, "$\\Delta M = -1.43$, 95\\% HDI $[-2.33, -0.54]$"
)
expect_identical(
ttest_paired_output$full
, "$\\Delta M = -1.43$, 95\\% HDI $[-2.33, -0.54]$, $\\mathrm{BF}_{\\textrm{10}} = 17.26$"
)
# Custom interval and MCMC error
set.seed(123)
ttest_interval <- apa_print(
ttest_paired
, iterations = 1000
, interval = function(x) quantile(x, probs = c(0.025, 0.975))
, interval_type = "CrI"
, mcmc_error = TRUE
)
expect_apa_results(
ttest_interval
, labels = list(
estimate = "$M$"
, conf.int = "95\\% CrI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
, mcmc.error = "$\\pm\\%$"
)
)
expect_identical(
ttest_interval$full
, "$M = -1.43$, 95\\% CrI $[-2.31, -0.44]$, $\\mathrm{BF}_{\\textrm{10}} = 17.26 \\pm 0.00\\%$"
)
# bf_r1
set.seed(123)
ttest_r1 <- apa_print(
ttest_paired
, bf_r1 = 3
)
expect_equivalent(
ttest_r1$stat
, "$\\mathrm{BF}_{\\textrm{10}} = 51.78$"
)
set.seed(123)
ttest_1r <- apa_print(
ttest_paired
, bf_1r = 3
)
expect_identical(ttest_r1, ttest_1r)
}
)
test_that(
"ttestBF(): Independent samples"
, {
set.seed(123)
ttest <- BayesFactor::ttestBF(x = sleep$extra[sleep$group == 1], y = sleep$extra[sleep$group == 2])
ttest_output <- apa_print(
ttest
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
ttest_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(ttest_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 1.27$")
expect_identical(ttest_output$esti, "$\\Delta M = -1.13$, 95\\% HDI $[-2.76, 0.46]$")
expect_identical(ttest_output$full, "$\\Delta M = -1.13$, 95\\% HDI $[-2.76, 0.46]$, $\\mathrm{BF}_{\\textrm{10}} = 1.27$")
# Formula method ----
set.seed(123L)
ttest_form <- BayesFactor::ttestBF(formula = extra ~ group, data = sleep)
ttest_form_output <- apa_print(
ttest_form
, central_tendency = median
, iterations = 10000
)
expect_identical(ttest_output, ttest_form_output)
set.seed(123)
ttest_onesided <- BayesFactor::ttestBF(x = sleep$extra[sleep$group == 1], y = sleep$extra[sleep$group == 2], nullInterval = c(0, Inf))
ttest_onesided_output <- apa_print(
ttest_onesided
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
ttest_onesided_output
, labels = list(
estimate = "$\\Delta M$"
, conf.int = "95\\% HDI"
, alternative = "$\\mathcal{H}_1$"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_apa_term(
ttest_onesided_output
, term = "interval"
, estimate = "$\\Delta M = 0.26$, 95\\% HDI $[0.00, 1.00]$"
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 0.17$"
)
expect_apa_term(
ttest_onesided_output
, term = "inverse_interval"
, estimate = "$\\Delta M = 0.26$, 95\\% HDI $[0.00, 1.00]$"
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 2.36$"
)
ttest_output <- apa_print(
ttest_onesided
, mcmc_error = FALSE
, central_tendency = median
, iterations = 10000
)
expect_null(ttest_output$table$mcmc.error)
}
)
test_that(
"correlationBF()"
, {
set.seed(123)
corr <- BayesFactor::correlationBF(
x = iris$Sepal.Width
, y = iris$Sepal.Length
)
corr_output <- apa_print(
corr
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
corr_output
, labels = list(
estimate = "$r$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(corr_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 0.51$")
expect_identical(corr_output$esti, "$r = -0.11$, 95\\% HDI $[-0.27, 0.04]$")
expect_identical(corr_output$full, "$r = -0.11$, 95\\% HDI $[-0.27, 0.04]$, $\\mathrm{BF}_{\\textrm{10}} = 0.51$")
}
)
test_that(
"proportionBF()"
, {
set.seed(123)
prop <- BayesFactor::proportionBF(y = 15, N = 25, p = .5)
prop_output <- apa_print(
prop
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
prop_output
, labels = list(
estimate = "$\\hat\\pi$"
, conf.int = "95\\% HDI"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_equivalent(prop_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 0.66$")
expect_identical(prop_output$esti, "$\\hat\\pi = .58$, 95\\% HDI $[.40, .74]$")
expect_identical(prop_output$full, "$\\hat\\pi = .58$, 95\\% HDI $[.40, .74]$, $\\mathrm{BF}_{\\textrm{10}} = 0.66$")
}
)
test_that(
"contingencyTableBF()"
, {
data(raceDolls, package = "BayesFactor")
set.seed(123)
cont <- BayesFactor::contingencyTableBF(raceDolls, sampleType = "indepMulti", fixedMargin = "cols")
cont_output <- apa_print(
cont
, central_tendency = median
, iterations = 10000
)
expect_apa_results(
cont_output
, labels = list(
statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_identical(cont_output$stat, "$\\mathrm{BF}_{\\textrm{10}} = 1.81$")
expect_identical(cont_output$full, cont_output$stat)
}
)
test_that(
"anovaBF()"
, {
data(puzzles, package = "BayesFactor")
set.seed(123)
anova_bf_main = BayesFactor::anovaBF(
RT ~ shape*color + ID
, data = puzzles
, whichRandom = "ID"
, whichModels = "withmain"
, progress = FALSE
)
anova_bf_main_output <- apa_print(anova_bf_main)
expect_apa_results(
anova_bf_main_output
, labels = list(
model = "Model"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
, mcmc.error = "$\\pm\\%$"
)
)
expect_apa_term(
anova_bf_main_output
, term = "shape_ID"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 2.84 \\pm 1.12\\%$"
)
set.seed(123)
anova_bf_top = BayesFactor::anovaBF(
RT ~ shape*color + ID
, data = puzzles
, whichRandom = "ID"
, whichModels = "top"
, progress = FALSE
)
anova_bf_top_output <- apa_print(anova_bf_top)
expect_apa_results(
anova_bf_top_output
, labels = list(
term = "Term"
, statistic = "$\\mathrm{BF}_{\\textrm{01}}$"
)
)
expect_apa_term(
anova_bf_top_output
, term = "color_shape"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.65$"
)
anova_bf_top_output <- apa_print(
anova_bf_top
, scientific_threshold = c(min = 0.99, max = 1.1)
)
expect_apa_term(
anova_bf_top_output
, term = "color_shape"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.65$"
)
expect_apa_term(
anova_bf_top_output
, term = "color"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.33 \\times 10^{-1}$"
)
anova_bf_top_output <- apa_print(anova_bf_top, log = TRUE)
expect_apa_results(
anova_bf_top_output
, labels = list(
term = "Term"
, statistic = "$\\log \\mathrm{BF}_{\\textrm{01}}$"
)
)
expect_apa_term(
anova_bf_top_output
, term = "color"
, estimate = NULL
, statistic = "$\\log \\mathrm{BF}_{\\textrm{01}} = -1.45$"
)
}
)
test_that(
"regressionBF()"
, {
data(attitude)
set.seed(123)
regression_bf_all <- BayesFactor::regressionBF(
rating ~ complaints + privileges + learning + raises
, data = attitude
, whichModels = "all"
, progress = FALSE
)
regression_bf_all_output <- apa_print(regression_bf_all)
expect_apa_results(
regression_bf_all_output
, labels = list(
model = "Model"
, statistic = "$\\mathrm{BF}_{\\textrm{10}}$"
)
)
expect_apa_term(
regression_bf_all_output
, term = "privileges_raises"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 25.90$"
)
regression_bf_top <- BayesFactor::regressionBF(
rating ~ complaints + privileges + learning + raises
, data = attitude
, whichModels = "top"
, progress = FALSE
)
regression_bf_top_output <- apa_print(regression_bf_top)
expect_apa_results(
regression_bf_top_output
, labels = list(
term = "Term"
, statistic = "$\\mathrm{BF}_{\\textrm{01}}$"
)
)
expect_apa_term(
regression_bf_top_output
, term = "complaints"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{01}} = 2.85 \\times 10^{-3}$"
)
regression_bf_top_output <- apa_print(regression_bf_top, reciprocal = TRUE)
expect_apa_term(
regression_bf_top_output
, term = "complaints"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 351.26$"
)
regression_bf_top_output <- apa_print(
regression_bf_top
, log = TRUE
, reciprocal = TRUE
)
expect_apa_term(
regression_bf_top_output
, term = "complaints"
, estimate = NULL
, statistic = "$\\log \\mathrm{BF}_{\\textrm{10}} = 5.86$"
)
}
)
test_that(
"lmBF()"
, {
set.seed(1298)
data(puzzles, package = "BayesFactor")
## Bayes factor of full model against null
bf_full <- BayesFactor::lmBF(
RT ~ shape + color + shape:color + ID + ID:shape + ID:color
, data = puzzles
, whichRandom = c("ID", "ID:shape", "ID:color")
)
expect_identical(
apa_print(bf_full)$statistic
, "$\\mathrm{BF}_{\\textrm{10}} = 3,421.93$"
)
# bf_shape <- lmBF(
# RT ~ color + shape:color + ID + ID:shape + ID:color
# , data = puzzles
# , whichRandom = c("ID", "ID:shape", "ID:color")
# )
# bf_color <- lmBF(
# RT ~ shape + shape:color + ID + ID:shape + ID:color
# , data = puzzles
# , whichRandom = c("ID", "ID:shape", "ID:color")
# )
# bf <- bf_full / c(bf_shape, bf_color)
# bf_res <- apa_print(bf)
}
)
test_that(
"generalTestBF()"
, {
set.seed(1298)
data(puzzles, package = "BayesFactor")
## Bayes factor of full model against null
bf <- BayesFactor::generalTestBF(
RT ~ shape * color + ID
, data = puzzles
, whichRandom = "ID"
, progress = FALSE
)
bf_res <- apa_print(bf)
expect_apa_term(
bf_res
, term = "ID"
, estimate = NULL
, statistic = "$\\mathrm{BF}_{\\textrm{10}} = 1.12 \\times 10^{5} \\pm 0.00\\%$"
)
}
)
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