Nothing
tests/testthat/test-aov_b.R
In bayesics: Bayesian Analyses for One- and Two-Sample Inference and Regression Methods
go_fast_for_cran_checks = TRUE
# Proper, heteroscedastic -------------------------------------------------
test_that("Proper prior and heteroscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters and mc_error
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01)
)
expect_equal(fitb$hyperparameters,
list(mu = mean(test_data$outcome),
nu = 0.001,
a = 0.001,
b = 0.001))
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Make sure contrasts work
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test aov_b fit
expect_no_error(
fite <-
aov_b(log(e_outcome) ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
expect_equal(fita$summary,
fite$summary)
## Make sure prediction function works
expect_no_error(
fite_preds <- predict(fite)
)
expect_no_error(predict(fite,
newdata = fite$data[1,]))
expect_gt(predict(fite,
newdata = fite$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fite,
newdata = fite$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fite,
newdata = fite$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fite,
newdata = fite$data[1,],
PI_level = 0.9)$PI_lower[1])
## Test plot
expect_s3_class(plot(fite),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
plan(sequential)
}
rm(list=ls())
})
# Proper, homoscedastic ---------------------------------------------------
test_that("Proper prior and homoscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01)
)
expect_equal(fitb$hyperparameters,
list(mu = mean(test_data$outcome),
nu = 0.001,
a = 0.001,
b = 0.001))
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Make sure contrasts work
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
plan(sequential)
}
})
# Improper prior, heteroscedastic -----------------------------------------
test_that("Imroper prior and heteroscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters DO NOT change anything
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
prior_mean_mu = 200,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
expect_equal(fita$summary,fitb$summary)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Test contrasts
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE)
)
plan(sequential)
}
rm(list=ls())
})
# Improper prior, homoscedastic -----------------------------------------
test_that("Imroper prior and homoscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters DO NOT change anything
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
prior_mean_mu = 200,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01,
heteroscedastic = FALSE)
)
expect_equal(fita$summary,fitb$summary)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Test contrasts
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE)
)
plan(sequential)
}
rm(list=ls())
})
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go_fast_for_cran_checks = TRUE
# Proper, heteroscedastic -------------------------------------------------
test_that("Proper prior and heteroscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters and mc_error
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01)
)
expect_equal(fitb$hyperparameters,
list(mu = mean(test_data$outcome),
nu = 0.001,
a = 0.001,
b = 0.001))
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Make sure contrasts work
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test aov_b fit
expect_no_error(
fite <-
aov_b(log(e_outcome) ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
expect_equal(fita$summary,
fite$summary)
## Make sure prediction function works
expect_no_error(
fite_preds <- predict(fite)
)
expect_no_error(predict(fite,
newdata = fite$data[1,]))
expect_gt(predict(fite,
newdata = fite$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fite,
newdata = fite$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fite,
newdata = fite$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fite,
newdata = fite$data[1,],
PI_level = 0.9)$PI_lower[1])
## Test plot
expect_s3_class(plot(fite),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
plan(sequential)
}
rm(list=ls())
})
# Proper, homoscedastic ---------------------------------------------------
test_that("Proper prior and homoscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01)
)
expect_equal(fitb$hyperparameters,
list(mu = mean(test_data$outcome),
nu = 0.001,
a = 0.001,
b = 0.001))
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Make sure contrasts work
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
prior_mean_mu = 2,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
plan(sequential)
}
})
# Improper prior, heteroscedastic -----------------------------------------
test_that("Imroper prior and heteroscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters DO NOT change anything
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
prior_mean_mu = 200,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01)
)
expect_equal(fita$summary,fitb$summary)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Test contrasts
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE)
)
plan(sequential)
}
rm(list=ls())
})
# Improper prior, homoscedastic -----------------------------------------
test_that("Imroper prior and homoscedastic model works", {
# Create data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome =
rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )
# No errors upon fitting
expect_no_error(
fita <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE)
)
# Make sure print works
expect_no_error(fita)
# Make sure summary.aov_b works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_type(s,"list")
expect_s3_class(s$summary,c("tbl_df", "tbl", "data.frame"))
expect_named(s$summary,c("Variable","Post Mean","Lower","Upper","Prob Dir"))
expect_type(s$summary$Variable,"character")
expect_type(s$summary$`Post Mean`,"double")
expect_type(s$summary$Lower,"double")
expect_type(s$summary$Upper,"double")
expect_type(s$summary$`Prob Dir`,"double")
expect_type(s$pairwise$Comparison,"character")
expect_type(s$pairwise$`Post Mean`,"double")
expect_type(s$pairwise$Lower,"double")
expect_type(s$pairwise$Upper,"double")
expect_type(unlist(s$pairwise[,5]),"double")
expect_type(s$pairwise$EPR,"double")
expect_type(s$pairwise$`EPR Lower`,"double")
expect_type(s$pairwise$`EPR Upper`,"double")
## Make sure coef.aov_b works
expect_type(coef(fita), "double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
expect_true(is.matrix(credint(fita,which = "pair")))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Test default hyperparameters DO NOT change anything
expect_no_error(
fitb <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
prior_mean_mu = 200,
prior_mean_nu = 0.5,
prior_var_shape = 0.01,
prior_var_rate = 0.01,
heteroscedastic = FALSE)
)
expect_equal(fita$summary,fitb$summary)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gt(predict(fita,CI_level = 0.8)$CI_lower[1],
predict(fita,CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fita,PI_level = 0.8)$PI_lower[1],
predict(fita,PI_level = 0.9)$PI_lower[1])
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Test contrasts
## One contrast
expect_no_error(
fitc <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1/3,-1/3,-1/3,1/2,1/2))
)
expect_named(fitc$contrasts,
c("L","summary"))
expect_s3_class(fitc$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitc$contrasts$summary$`Post Mean`,"double")
expect_type(fitc$contrasts$summary$Lower,"double")
expect_type(fitc$contrasts$summary$Upper,"double")
## Multiple contrasts
expect_no_error(
fitd <-
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = rbind(c(-1/3,-1/3,-1/3,1/2,1/2),
c(-1/3,-1/3,-1/3,1,0)))
)
expect_named(fitd$contrasts,
c("L","summary"))
expect_s3_class(fitd$contrasts$summary,c("tbl_df", "tbl", "data.frame"))
expect_type(fitd$contrasts$summary$`Post Mean`,"double")
expect_type(fitd$contrasts$summary$Lower,"double")
expect_type(fitd$contrasts$summary$Upper,"double")
## Invalid contrasts should throw an error
expect_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE,
mc_error = 0.01,
contrasts = c(-1,-1,-1,1,1))
)
# Make sure plotting function works
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
PI_level = 0.8),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
CI_level = 0.999),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Make sure parallelization works
if(!go_fast_for_cran_checks){
plan(multisession,workers = 5)
expect_no_error(
aov_b(outcome ~ x1,
test_data,
improper = TRUE,
heteroscedastic = FALSE)
)
plan(sequential)
}
rm(list=ls())
})
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