Nothing
tests/testthat/test-lm_b.R
In bayesics: Bayesian Analyses for One- and Two-Sample Inference and Regression Methods
test_that("Test lm_b with conjugate prior",{
# Generate some data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")) )
# Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj")
)
# Test CI_level
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj",
CI_level = 0.9)
)
expect_lt(fita$summary$Lower[1],
fitb$summary$Lower[1])
# Make sure print works
expect_no_error(fita)
# Make sure coef works
expect_type(coef(fita),"double")
# Make sure credint works
expect_true(is.matrix(credint(fita)))
# Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Make sure summary works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
# Check fractional Bayes factors
expect_no_error(
fita_no_x1 <-
lm_b(outcome ~ x2 + x3,
data = test_data,
prior = "conj")
)
expect_no_error(
fbf <-
frac_bayes_factors(fita,
fita_no_x1)
)
## Check output format
expect_s3_class(fbf,c("tbl_df", "tbl", "data.frame"))
## Check result is reasonable
expect_true(fbf$`fractional BF` > 0)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
# Make sure savage-dickey ratio works
expect_s3_class(bayes_factors(fita),
c("tbl_df", "tbl", "data.frame"))
expect_s3_class(bayes_factors(fita,by = "v"),
c("tbl_df", "tbl", "data.frame"))
# 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 number and type of inputs
expect_no_error(
lm_b(test_data$outcome ~ test_data$x1,
prior = "conjugate")
)
expect_no_error(
lm_b(test_data$outcome ~ 1,
prior = "conju")
)
expect_no_error(
lm_b(outcome ~ x1,
data = test_data,
prior = "con")
)
expect_no_error(
lm_b(outcome ~ 1,
data = test_data,
prior = "co")
)
# Test hyperparameters
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj",
prior_beta_mean = rep(2,7),
prior_beta_precision = diag(7) * 0.1,
prior_var_shape = 2,
prior_var_rate = 2)
)
expect_equal(fitc$hyperparameters$mu, rep(2,7))
expect_equal(fitc$hyperparameters$a, 2)
expect_equal(fitc$hyperparameters$b, 2)
expect_equal(fitc$hyperparameters$V, diag(7) * 0.1)
expect_true(!isTRUE(all.equal(fita$summary,
fitc$summary)))
# Test plot
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
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 = c("cr","pr"),
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
variable = "x1"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
variable = "x1"),
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),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test lm_b fit
expect_no_error(
fitc <-
lm_b(log(e_outcome) ~ x1 + x2 + x3,
data = test_data,
prior = "conj")
)
expect_equal(fita$summary,
fitc$summary)
## Make sure print works
expect_no_error(fitc)
## Make sure coef works
expect_type(coef(fitc),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitc)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitc)))
## Make sure summary works
expect_no_error(
s <-
summary(fitc)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(
fitc_preds <- predict(fitc)
)
expect_no_error(predict(fitc,
newdata = fitc$data[1,]))
expect_gt(predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.9)$PI_lower[1])
## Check plot
expect_s3_class(plot(fitc,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitc),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
test_that("Test lm_b with zellner's g prior",{
# Generate some data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")) )
# Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "ze")
)
# Test CI_level
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "ze",
CI_level = 0.9)
)
expect_lt(fita$summary$Lower[1],
fitb$summary$Lower[1])
# Make sure print works
expect_no_error(fita)
# Make sure coef works
expect_type(coef(fita),"double")
# Make sure credint works
expect_true(is.matrix(credint(fita)))
# Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Make sure summary works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
# Check fractional Bayes factors
expect_no_error(
fita_no_x1 <-
lm_b(outcome ~ x2 + x3,
data = test_data,
prior = "z")
)
expect_no_error(
fbf <-
frac_bayes_factors(fita,
fita_no_x1)
)
## Check output format
expect_s3_class(fbf,c("tbl_df", "tbl", "data.frame"))
## Check result is reasonable
expect_true(fbf$`fractional BF` > 0)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
# Make sure savage-dickey ratio works
expect_s3_class(bayes_factors(fita),
c("tbl_df", "tbl", "data.frame"))
expect_s3_class(bayes_factors(fita,by = "v"),
c("tbl_df", "tbl", "data.frame"))
# 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 number and type of inputs
expect_no_error(
lm_b(test_data$outcome ~ test_data$x1,
prior = "zellner")
)
expect_no_error(
lm_b(test_data$outcome ~ 1,
prior = "zellne")
)
expect_no_error(
lm_b(outcome ~ x1,
data = test_data,
prior = "zell")
)
expect_no_error(
lm_b(outcome ~ 1,
data = test_data,
prior = "z")
)
# Test hyperparameters
## Test beta hyperparameters do NOT affect anything for zellner's g
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "z",
prior_beta_mean = rep(2,7),
prior_beta_precision = diag(7) * 0.1)
)
expect_true(isTRUE(all.equal(fita$summary,
fitc$summary)))
## Test g
expect_no_error(
fitd <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "z",
zellner_g = 10)
)
expect_true(!isTRUE(all.equal(fita$summary,
fitd$summary)))
# Test plot
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
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 = c("cr","pr"),
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
variable = "x1"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
variable = "x1"),
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),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
test_that("Test lm_b with improper prior",{
# Generate some data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")) )
# Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "improper")
)
# Test CI_level
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "imp",
CI_level = 0.9)
)
expect_lt(fita$summary$Lower[1],
fitb$summary$Lower[1])
# Make sure print works
expect_no_error(fita)
# Make sure coef works
expect_type(coef(fita),"double")
# Make sure credint works
expect_true(is.matrix(credint(fita)))
# Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Make sure summary works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
# Check fractional Bayes factors
expect_no_error(
fita_no_x1 <-
lm_b(outcome ~ x2 + x3,
data = test_data,
prior = "i")
)
expect_no_error(
fbf <-
frac_bayes_factors(fita,
fita_no_x1)
)
## Check output format
expect_s3_class(fbf,c("tbl_df", "tbl", "data.frame"))
## Check result is reasonable
expect_true(fbf$`fractional BF` > 0)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
# Make sure savage-dickey ratio does NOT work
expect_error(bayes_factors(fita))
# 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 number and type of inputs
expect_no_error(
lm_b(test_data$outcome ~ test_data$x1,
prior = "improper")
)
expect_no_error(
lm_b(test_data$outcome ~ 1,
prior = "improp")
)
expect_no_error(
lm_b(outcome ~ x1,
data = test_data,
prior = "imp")
)
expect_no_error(
lm_b(outcome ~ 1,
data = test_data,
prior = "i")
)
# Test hyperparameters
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "imp",
prior_beta_mean = rep(2,7),
prior_beta_precision = diag(7) * 0.1,
prior_var_shape = 2,
prior_var_rate = 2)
)
expect_true(is.na(fitc$hyperparameters))
expect_true(isTRUE(all.equal(fita$summary,
fitc$summary)))
# Test plot
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
variable = "x1"),
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",
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
variable = "x1"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test lm_b fit
expect_no_error(
fitc <-
lm_b(log(e_outcome) ~ x1 + x2 + x3,
data = test_data,
prior = "imp")
)
expect_equal(fita$summary,
fitc$summary)
## Make sure print works
expect_no_error(fitc)
## Make sure coef works
expect_type(coef(fitc),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitc)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitc)))
## Make sure summary works
expect_no_error(
s <-
summary(fitc)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
## Make sure prediction function works
expect_no_error(
fitc_preds <- predict(fitc)
)
expect_no_error(predict(fitc,
newdata = fitc$data[1,]))
expect_gt(predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.9)$PI_lower[1])
## Check plot
expect_s3_class(plot(fitc,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitc),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
test_that("Test lm_b with weights",{
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5],
w = rbeta(N,2,2))
test_data$outcome =
rnorm(N,
-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")),
1.0 / sqrt(test_data$w))
# Conjugate prior
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = test_data$w,
prior = "conj")
)
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = "w",
prior = "conj")
)
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj")
)
expect_equal(fita$summary,
fitb$summary)
expect_true(!isTRUE(all.equal(fita$summary,
fitc$summary)))
# Zellner's g prior
expect_no_error(
fitd <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = test_data$w,
prior = "z")
)
expect_no_error(
fite <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = "w",
prior = "z")
)
expect_no_error(
fitf <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "z")
)
expect_equal(fitd$summary,
fite$summary)
expect_true(!isTRUE(all.equal(fitd$summary,
fitf$summary)))
# Improper prior
expect_no_error(
fitg <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = test_data$w,
prior = "i")
)
expect_no_error(
fith <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = "w",
prior = "i")
)
expect_no_error(
fiti <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "i")
)
expect_equal(fitg$summary,
fith$summary)
expect_true(!isTRUE(all.equal(fitg$summary,
fiti$summary)))
})
test_that("Test complicated terms in lm_b formula",{
# I(...) in covariate and interactions
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 +
test_data$x1 +
0.25 * I(test_data$x1^2) -
2 * (test_data$x3 %in% c("d","e")) )
## Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 * x2 + x3 + I(x1^2),
data = test_data,
prior = "conj")
)
## Make sure print works
expect_no_error(fita)
## Make sure coef works
expect_type(coef(fita),"double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
## Make sure summary works
expect_no_error(
s <-
summary(fita)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fita)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)","x1","x2","x3b","x3c","x3d","x3e","I(x1^2)","x1:x2"))
expect_no_error(
sd2 <-
bayes_factors(fita, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2","x3"))
## 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 plot
expect_s3_class(plot(fita,
type = "pdp"),
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"))
# Check if splines terms work
library(splines)
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + test_data$x1^2 + 2 * (test_data$x3 %in% c("d","e")) )
## Test lm_b fit
expect_no_error(
fitb <-
lm_b(outcome ~ ns(x1,df = 5) + x2,
data = test_data,
prior = "conj")
)
## Make sure print works
expect_no_error(fitb)
## Make sure coef works
expect_type(coef(fitb),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitb)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitb)))
## Make sure summary works
expect_no_error(
s <-
summary(fitb)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(predict(fitb))
expect_no_error(predict(fitb,
newdata = fitb$data[1,]))
expect_gte(predict(fitb,
newdata = fitb$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitb,
newdata = fitb$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fitb,
newdata = fitb$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitb,
newdata = fitb$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fitb)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)",
paste("ns(x1, df = 5)",1:5,sep=""),
"x2"))
expect_no_error(
sd2 <-
bayes_factors(fitb, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2"))
## Make sure information criteria work
expect_type(AIC(fitb),"double")
expect_type(BIC(fitb),"double")
expect_type(DIC(fitb),"double")
expect_type(WAIC(fitb),"double")
## Test plot
expect_s3_class(plot(fitb,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitb),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test lm_b fit
expect_no_error(
fitc <-
lm_b(log(e_outcome) ~ x1 * x2 + x3 + I(x1^2),
data = test_data,
prior = "conj")
)
expect_no_error(
fitc2 <-
lm_b(outcome ~ x1 * x2 + x3 + I(x1^2),
data = test_data,
prior = "conj")
)
expect_equal(fitc$summary,
fitc2$summary)
## Make sure print works
expect_no_error(fitc)
## Make sure coef works
expect_type(coef(fitc),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitc)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitc)))
## Make sure summary works
expect_no_error(
s <-
summary(fitc)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(
fitc_preds <- predict(fitc)
)
expect_no_error(predict(fitc,
newdata = fitc$data[1,]))
expect_gte(predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fitc)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)","x1","x2","x3b","x3c","x3d","x3e","I(x1^2)","x1:x2"))
expect_no_error(
sd2 <-
bayes_factors(fitc, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2","x3"))
## Make sure information criteria work
expect_type(AIC(fitc),"double")
expect_type(BIC(fitc),"double")
expect_type(DIC(fitc),"double")
expect_type(WAIC(fitc),"double")
## Test plot
expect_s3_class(plot(fitc,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitc),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# ~ .
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 +
test_data$x1 +
2 * (test_data$x3 %in% c("d","e")) )
## Test lm_b fit
expect_no_error(
fitd <-
lm_b(outcome ~ .,
data = test_data,
prior = "conj")
)
## Make sure print works
expect_no_error(fitd)
## Make sure coef works
expect_type(coef(fitd),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitd)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitd)))
## Make sure summary works
expect_no_error(
s <-
summary(fitd)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(predict(fitd))
expect_no_error(predict(fitd,
newdata = fitd$data[1,]))
expect_gte(predict(fitd,
newdata = fitd$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitd,
newdata = fitd$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fitd,
newdata = fitd$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitd,
newdata = fitd$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fitd)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)","x1","x2","x3b","x3c","x3d","x3e"))
expect_no_error(
sd2 <-
bayes_factors(fitd, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2","x3"))
## Make sure information criteria work
expect_type(AIC(fitd),"double")
expect_type(BIC(fitd),"double")
expect_type(DIC(fitd),"double")
expect_type(WAIC(fitd),"double")
## Test plot
expect_s3_class(plot(fitd,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitd),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
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test_that("Test lm_b with conjugate prior",{
# Generate some data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")) )
# Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj")
)
# Test CI_level
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj",
CI_level = 0.9)
)
expect_lt(fita$summary$Lower[1],
fitb$summary$Lower[1])
# Make sure print works
expect_no_error(fita)
# Make sure coef works
expect_type(coef(fita),"double")
# Make sure credint works
expect_true(is.matrix(credint(fita)))
# Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Make sure summary works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
# Check fractional Bayes factors
expect_no_error(
fita_no_x1 <-
lm_b(outcome ~ x2 + x3,
data = test_data,
prior = "conj")
)
expect_no_error(
fbf <-
frac_bayes_factors(fita,
fita_no_x1)
)
## Check output format
expect_s3_class(fbf,c("tbl_df", "tbl", "data.frame"))
## Check result is reasonable
expect_true(fbf$`fractional BF` > 0)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
# Make sure savage-dickey ratio works
expect_s3_class(bayes_factors(fita),
c("tbl_df", "tbl", "data.frame"))
expect_s3_class(bayes_factors(fita,by = "v"),
c("tbl_df", "tbl", "data.frame"))
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Test number and type of inputs
expect_no_error(
lm_b(test_data$outcome ~ test_data$x1,
prior = "conjugate")
)
expect_no_error(
lm_b(test_data$outcome ~ 1,
prior = "conju")
)
expect_no_error(
lm_b(outcome ~ x1,
data = test_data,
prior = "con")
)
expect_no_error(
lm_b(outcome ~ 1,
data = test_data,
prior = "co")
)
# Test hyperparameters
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj",
prior_beta_mean = rep(2,7),
prior_beta_precision = diag(7) * 0.1,
prior_var_shape = 2,
prior_var_rate = 2)
)
expect_equal(fitc$hyperparameters$mu, rep(2,7))
expect_equal(fitc$hyperparameters$a, 2)
expect_equal(fitc$hyperparameters$b, 2)
expect_equal(fitc$hyperparameters$V, diag(7) * 0.1)
expect_true(!isTRUE(all.equal(fita$summary,
fitc$summary)))
# Test plot
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
variable = "x1"),
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",
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
variable = "x1"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test lm_b fit
expect_no_error(
fitc <-
lm_b(log(e_outcome) ~ x1 + x2 + x3,
data = test_data,
prior = "conj")
)
expect_equal(fita$summary,
fitc$summary)
## Make sure print works
expect_no_error(fitc)
## Make sure coef works
expect_type(coef(fitc),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitc)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitc)))
## Make sure summary works
expect_no_error(
s <-
summary(fitc)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(
fitc_preds <- predict(fitc)
)
expect_no_error(predict(fitc,
newdata = fitc$data[1,]))
expect_gt(predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.9)$PI_lower[1])
## Check plot
expect_s3_class(plot(fitc,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitc),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
test_that("Test lm_b with zellner's g prior",{
# Generate some data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")) )
# Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "ze")
)
# Test CI_level
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "ze",
CI_level = 0.9)
)
expect_lt(fita$summary$Lower[1],
fitb$summary$Lower[1])
# Make sure print works
expect_no_error(fita)
# Make sure coef works
expect_type(coef(fita),"double")
# Make sure credint works
expect_true(is.matrix(credint(fita)))
# Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Make sure summary works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
# Check fractional Bayes factors
expect_no_error(
fita_no_x1 <-
lm_b(outcome ~ x2 + x3,
data = test_data,
prior = "z")
)
expect_no_error(
fbf <-
frac_bayes_factors(fita,
fita_no_x1)
)
## Check output format
expect_s3_class(fbf,c("tbl_df", "tbl", "data.frame"))
## Check result is reasonable
expect_true(fbf$`fractional BF` > 0)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
# Make sure savage-dickey ratio works
expect_s3_class(bayes_factors(fita),
c("tbl_df", "tbl", "data.frame"))
expect_s3_class(bayes_factors(fita,by = "v"),
c("tbl_df", "tbl", "data.frame"))
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Test number and type of inputs
expect_no_error(
lm_b(test_data$outcome ~ test_data$x1,
prior = "zellner")
)
expect_no_error(
lm_b(test_data$outcome ~ 1,
prior = "zellne")
)
expect_no_error(
lm_b(outcome ~ x1,
data = test_data,
prior = "zell")
)
expect_no_error(
lm_b(outcome ~ 1,
data = test_data,
prior = "z")
)
# Test hyperparameters
## Test beta hyperparameters do NOT affect anything for zellner's g
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "z",
prior_beta_mean = rep(2,7),
prior_beta_precision = diag(7) * 0.1)
)
expect_true(isTRUE(all.equal(fita$summary,
fitc$summary)))
## Test g
expect_no_error(
fitd <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "z",
zellner_g = 10)
)
expect_true(!isTRUE(all.equal(fita$summary,
fitd$summary)))
# Test plot
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
variable = "x1"),
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",
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
variable = "x1"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
test_that("Test lm_b with improper prior",{
# Generate some data
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")) )
# Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "improper")
)
# Test CI_level
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "imp",
CI_level = 0.9)
)
expect_lt(fita$summary$Lower[1],
fitb$summary$Lower[1])
# Make sure print works
expect_no_error(fita)
# Make sure coef works
expect_type(coef(fita),"double")
# Make sure credint works
expect_true(is.matrix(credint(fita)))
# Make sure vcov works
expect_true(is.matrix(vcov(fita)))
# Make sure summary works
expect_no_error(
s <-
summary(fita)
)
## Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
# Check fractional Bayes factors
expect_no_error(
fita_no_x1 <-
lm_b(outcome ~ x2 + x3,
data = test_data,
prior = "i")
)
expect_no_error(
fbf <-
frac_bayes_factors(fita,
fita_no_x1)
)
## Check output format
expect_s3_class(fbf,c("tbl_df", "tbl", "data.frame"))
## Check result is reasonable
expect_true(fbf$`fractional BF` > 0)
# Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
# Make sure savage-dickey ratio does NOT work
expect_error(bayes_factors(fita))
# Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
# Test number and type of inputs
expect_no_error(
lm_b(test_data$outcome ~ test_data$x1,
prior = "improper")
)
expect_no_error(
lm_b(test_data$outcome ~ 1,
prior = "improp")
)
expect_no_error(
lm_b(outcome ~ x1,
data = test_data,
prior = "imp")
)
expect_no_error(
lm_b(outcome ~ 1,
data = test_data,
prior = "i")
)
# Test hyperparameters
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "imp",
prior_beta_mean = rep(2,7),
prior_beta_precision = diag(7) * 0.1,
prior_var_shape = 2,
prior_var_rate = 2)
)
expect_true(is.na(fitc$hyperparameters))
expect_true(isTRUE(all.equal(fita$summary,
fitc$summary)))
# Test plot
expect_s3_class(plot(fita,
type = "diagnostics"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
variable = "x1",
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = c("cr","pr"),
combine_pi_ci = TRUE,
exemplar_covariates = fita$data[1,]),
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 = c("cr","pr"),
combine_pi_ci = FALSE,
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
variable = "x1"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "pr",
exemplar_covariates = fita$data[1,]),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita,
type = "cr",
variable = "x1"),
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),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test lm_b fit
expect_no_error(
fitc <-
lm_b(log(e_outcome) ~ x1 + x2 + x3,
data = test_data,
prior = "imp")
)
expect_equal(fita$summary,
fitc$summary)
## Make sure print works
expect_no_error(fitc)
## Make sure coef works
expect_type(coef(fitc),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitc)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitc)))
## Make sure summary works
expect_no_error(
s <-
summary(fitc)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
## Make sure prediction function works
expect_no_error(
fitc_preds <- predict(fitc)
)
expect_no_error(predict(fitc,
newdata = fitc$data[1,]))
expect_gt(predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gt(predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.9)$PI_lower[1])
## Check plot
expect_s3_class(plot(fitc,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitc),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
test_that("Test lm_b with weights",{
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5],
w = rbeta(N,2,2))
test_data$outcome =
rnorm(N,
-1 + test_data$x1 + 2 * (test_data$x3 %in% c("d","e")),
1.0 / sqrt(test_data$w))
# Conjugate prior
expect_no_error(
fita <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = test_data$w,
prior = "conj")
)
expect_no_error(
fitb <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = "w",
prior = "conj")
)
expect_no_error(
fitc <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "conj")
)
expect_equal(fita$summary,
fitb$summary)
expect_true(!isTRUE(all.equal(fita$summary,
fitc$summary)))
# Zellner's g prior
expect_no_error(
fitd <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = test_data$w,
prior = "z")
)
expect_no_error(
fite <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = "w",
prior = "z")
)
expect_no_error(
fitf <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "z")
)
expect_equal(fitd$summary,
fite$summary)
expect_true(!isTRUE(all.equal(fitd$summary,
fitf$summary)))
# Improper prior
expect_no_error(
fitg <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = test_data$w,
prior = "i")
)
expect_no_error(
fith <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
weights = "w",
prior = "i")
)
expect_no_error(
fiti <-
lm_b(outcome ~ x1 + x2 + x3,
data = test_data,
prior = "i")
)
expect_equal(fitg$summary,
fith$summary)
expect_true(!isTRUE(all.equal(fitg$summary,
fiti$summary)))
})
test_that("Test complicated terms in lm_b formula",{
# I(...) in covariate and interactions
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 +
test_data$x1 +
0.25 * I(test_data$x1^2) -
2 * (test_data$x3 %in% c("d","e")) )
## Test lm_b fit
expect_no_error(
fita <-
lm_b(outcome ~ x1 * x2 + x3 + I(x1^2),
data = test_data,
prior = "conj")
)
## Make sure print works
expect_no_error(fita)
## Make sure coef works
expect_type(coef(fita),"double")
## Make sure credint works
expect_true(is.matrix(credint(fita)))
## Make sure vcov works
expect_true(is.matrix(vcov(fita)))
## Make sure summary works
expect_no_error(
s <-
summary(fita)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(predict(fita))
expect_no_error(predict(fita,
newdata = fita$data[1,]))
expect_gte(predict(fita,
newdata = fita$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fita,
newdata = fita$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fita,
newdata = fita$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fita,
newdata = fita$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fita)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)","x1","x2","x3b","x3c","x3d","x3e","I(x1^2)","x1:x2"))
expect_no_error(
sd2 <-
bayes_factors(fita, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2","x3"))
## Make sure information criteria work
expect_type(AIC(fita),"double")
expect_type(BIC(fita),"double")
expect_type(DIC(fita),"double")
expect_type(WAIC(fita),"double")
## Test plot
expect_s3_class(plot(fita,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fita),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Check if splines terms work
library(splines)
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 + test_data$x1 + test_data$x1^2 + 2 * (test_data$x3 %in% c("d","e")) )
## Test lm_b fit
expect_no_error(
fitb <-
lm_b(outcome ~ ns(x1,df = 5) + x2,
data = test_data,
prior = "conj")
)
## Make sure print works
expect_no_error(fitb)
## Make sure coef works
expect_type(coef(fitb),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitb)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitb)))
## Make sure summary works
expect_no_error(
s <-
summary(fitb)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(predict(fitb))
expect_no_error(predict(fitb,
newdata = fitb$data[1,]))
expect_gte(predict(fitb,
newdata = fitb$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitb,
newdata = fitb$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fitb,
newdata = fitb$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitb,
newdata = fitb$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fitb)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)",
paste("ns(x1, df = 5)",1:5,sep=""),
"x2"))
expect_no_error(
sd2 <-
bayes_factors(fitb, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2"))
## Make sure information criteria work
expect_type(AIC(fitb),"double")
expect_type(BIC(fitb),"double")
expect_type(DIC(fitb),"double")
expect_type(WAIC(fitb),"double")
## Test plot
expect_s3_class(plot(fitb,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitb),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# Test if response transformation works
test_data$e_outcome = exp(test_data$outcome)
## Test lm_b fit
expect_no_error(
fitc <-
lm_b(log(e_outcome) ~ x1 * x2 + x3 + I(x1^2),
data = test_data,
prior = "conj")
)
expect_no_error(
fitc2 <-
lm_b(outcome ~ x1 * x2 + x3 + I(x1^2),
data = test_data,
prior = "conj")
)
expect_equal(fitc$summary,
fitc2$summary)
## Make sure print works
expect_no_error(fitc)
## Make sure coef works
expect_type(coef(fitc),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitc)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitc)))
## Make sure summary works
expect_no_error(
s <-
summary(fitc)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(
fitc_preds <- predict(fitc)
)
expect_no_error(predict(fitc,
newdata = fitc$data[1,]))
expect_gte(predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitc,
newdata = fitc$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fitc)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)","x1","x2","x3b","x3c","x3d","x3e","I(x1^2)","x1:x2"))
expect_no_error(
sd2 <-
bayes_factors(fitc, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2","x3"))
## Make sure information criteria work
expect_type(AIC(fitc),"double")
expect_type(BIC(fitc),"double")
expect_type(DIC(fitc),"double")
expect_type(WAIC(fitc),"double")
## Test plot
expect_s3_class(plot(fitc,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitc),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
# ~ .
set.seed(2025)
N = 500
test_data =
data.frame(x1 = rnorm(N),
x2 = rnorm(N),
x3 = letters[1:5])
test_data$outcome =
rnorm(N,-1 +
test_data$x1 +
2 * (test_data$x3 %in% c("d","e")) )
## Test lm_b fit
expect_no_error(
fitd <-
lm_b(outcome ~ .,
data = test_data,
prior = "conj")
)
## Make sure print works
expect_no_error(fitd)
## Make sure coef works
expect_type(coef(fitd),"double")
## Make sure credint works
expect_true(is.matrix(credint(fitd)))
## Make sure vcov works
expect_true(is.matrix(vcov(fitd)))
## Make sure summary works
expect_no_error(
s <-
summary(fitd)
)
### Check output format
expect_s3_class(s,c("tbl_df", "tbl", "data.frame"))
expect_identical(colnames(s),
c("Variable","Post Mean","Lower","Upper","Prob Dir",
"ROPE","ROPE bounds","BF favoring alternative",
"Interpretation"))
expect_type(s$Variable,"character")
expect_type(s$`Post Mean`,"double")
expect_type(s$Lower,"double")
expect_type(s$Upper,"double")
expect_type(s$`Prob Dir`,"double")
expect_type(s$ROPE,"double")
expect_type(s$`ROPE bounds`,"character")
expect_type(s$`BF favoring alternative`,"double")
expect_type(s$Interpretation,"character")
## Make sure prediction function works
expect_no_error(predict(fitd))
expect_no_error(predict(fitd,
newdata = fitd$data[1,]))
expect_gte(predict(fitd,
newdata = fitd$data[1,],
CI_level = 0.8)$CI_lower[1],
predict(fitd,
newdata = fitd$data[1,],
CI_level = 0.9)$CI_lower[1])
expect_gte(predict(fitd,
newdata = fitd$data[1,],
PI_level = 0.8)$PI_lower[1],
predict(fitd,
newdata = fitd$data[1,],
PI_level = 0.9)$PI_lower[1])
## Make sure savage-dickey ratio works
expect_no_error(
sd1 <-
bayes_factors(fitd)
)
expect_s3_class(sd1,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd1$Variable,
c("(Intercept)","x1","x2","x3b","x3c","x3d","x3e"))
expect_no_error(
sd2 <-
bayes_factors(fitd, by = "variabl")
)
expect_s3_class(sd2,
c("tbl_df", "tbl", "data.frame"))
expect_identical(sd2$Variable,
c("x1","x2","x3"))
## Make sure information criteria work
expect_type(AIC(fitd),"double")
expect_type(BIC(fitd),"double")
expect_type(DIC(fitd),"double")
expect_type(WAIC(fitd),"double")
## Test plot
expect_s3_class(plot(fitd,
type = "pdp"),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
expect_s3_class(plot(fitd),
c("patchwork","ggplot2::ggplot","ggplot",
"ggplot2::gg","S7_object","gg"))
})
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