Nothing
tests/testthat/test_boot_covariance.R
In robmed: (Robust) Mediation Analysis
context("bootstrap test: covariance fit")
## load package
library("robmed", quietly = TRUE)
## control parameters
n <- 250 # number of observations
a <- c <- 0.2 # true effects
b <- 0 # true effect
seed <- 20150601 # seed for the random number generator
## set seed for reproducibility
set.seed(seed)
## generate data
X <- rnorm(n)
M <- a * X + rnorm(n)
Y <- b * M + c * X + rnorm(n)
test_data <- data.frame(X, Y, M)
## arguments for bootstrap tests
x <- "X" # independent variable
y <- "Y" # dependent variable
m <- "M" # mediator variable
covariates <- character() # control variables
R <- 100 # number of bootstrap samples
level <- c(0.9, 0.95) # confidence level
ci_type <- "perc" # type of confidence intervals
ctrl <- cov_control(prob = 0.9) # for winsorized covariance matrix
## perform bootstrap tests
boot_list <- list(
winsorized = {
set.seed(seed)
test_mediation(test_data, x = x, y = y, m = m, test = "boot", R = R,
level = level[1], type = ci_type, method = "covariance",
robust = TRUE, control = ctrl)
},
ML = {
set.seed(seed)
test_mediation(test_data, x = x, y = y, m = m, test = "boot", R = R,
level = level[1], type = ci_type, method = "covariance",
robust = FALSE)
}
)
## correct values
effect_names <- c("a", "b", "Total", "Direct", "Indirect")
## run tests
# loop over methods
methods <- names(boot_list)
for (method in methods) {
# extract information for current method
boot <- boot_list[[method]]
# compute summaries
summary_list <- list(boot = summary(boot, type = "boot"),
data = summary(boot, type = "data"))
## run tests
test_that("output has correct structure", {
# bootstrap test
expect_s3_class(boot, "boot_test_mediation")
expect_s3_class(boot, "test_mediation")
# model fit
expect_s3_class(boot$fit, "cov_fit_mediation")
expect_s3_class(boot$fit, "fit_mediation")
# bootstrap replicates
expect_s3_class(boot$reps, "boot")
})
test_that("arguments are correctly passed", {
# alternative hypothesis
expect_identical(boot$alternative, "twosided")
# number of bootstrap replicates
# (doesn't hold for some methods if there are computational issues)
expect_identical(boot$R, as.integer(R))
# confidence level
expect_identical(boot$level, level[1])
# type of confidence intervals
expect_identical(boot$type, ci_type)
# variable names
expect_identical(boot$fit$x, x)
expect_identical(boot$fit$y, y)
expect_identical(boot$fit$m, m)
expect_identical(boot$fit$covariates, covariates)
# robust or nonrobust fit and test
if (method == "winsorized") {
expect_true(boot$fit$robust)
expect_equal(boot$fit$control, ctrl)
} else {
expect_false(boot$fit$robust)
expect_null(boot$fit$control)
}
})
test_that("dimensions are correct", {
# effects are scalars
expect_length(boot$indirect, 1L)
# only one indirect effect, so only one confidence interval
expect_length(boot$ci, 2L)
expect_named(boot$ci, c("Lower", "Upper"))
# dimensions of bootstrap replicates
expect_identical(dim(boot$reps$t), c(as.integer(R), 5L))
})
test_that("values of coefficients are correct", {
expect_equivalent(boot$a, mean(boot$reps$t[, 1]))
expect_equivalent(boot$b, mean(boot$reps$t[, 2]))
expect_null(boot[["d"]])
expect_equivalent(boot$direct, mean(boot$reps$t[, 4]))
expect_equivalent(boot$indirect, mean(boot$reps$t[, 5]))
expect_equivalent(boot$reps$t[, 5], boot$reps$t[, 1] * boot$reps$t[, 2])
# total effect
expect_equivalent(boot$total, mean(boot$reps$t[, 3]))
expect_equivalent(boot$reps$t[, 3], rowSums(boot$reps$t[, 4:5]))
})
# loop over types of estimation (bootstrap or on original data)
types <- names(summary_list)
for (type in types) {
# run tests
test_that("output of coef() method has correct attributes", {
# extract coefficients
coef <- coef(boot, type = type)
# tests
expect_length(coef, 5L)
expect_named(coef, effect_names)
})
test_that("coef() method returns correct values of coefficients", {
# object containing the true values
true <- if (type == "boot") boot else boot$fit
# tests
expect_equivalent(coef(boot, parm = "a", type = type),
true$a)
expect_equivalent(coef(boot, parm = "b", type = type),
true$b)
expect_null(coef(boot, parm = "d", type = type))
expect_equivalent(coef(boot, parm = "total", type = type),
true$total)
expect_equivalent(coef(boot, parm = "direct", type = type),
true$direct)
expect_equivalent(coef(boot, parm = "indirect", type = type),
true$indirect)
})
test_that("output of confint() method has correct attributes", {
# extract confidence intervals
ci <- confint(boot, type = type)
# tests
expect_equal(dim(ci), c(5L, 2L))
expect_equal(rownames(ci), effect_names)
expect_equal(colnames(ci), c("5 %", "95 %"))
})
test_that("confint() method returns correct values of confidence intervals", {
# confidence interval of indirect effect
ci_indirect <- confint(boot, parm = "indirect", type = type)
expect_equivalent(ci_indirect, boot$ci)
# extract confidence intervals for other effects
keep <- c("a", "b", "total", "direct")
ci_other <- confint(boot, parm = keep, type = type)
coef_other <- coef(boot, parm = keep, type = type)
expect_equivalent(rowMeans(ci_other), coef_other)
})
test_that("output of p_value() method has correct attributes", {
# extract p-value
digits <- 3
p_val <- p_value(boot, type = type, digits = digits)
# tests
expect_length(p_val, 5L)
expect_named(p_val, effect_names)
expect_equal(p_val["Indirect"], round(p_val["Indirect"], digits = digits))
})
# extract current summary
summary <- summary_list[[type]]
# correct values
if (type == "boot") {
n_col <- 5L
col_names <- c("Data", "Boot")
keep <- 1:2
} else {
n_col <- 4L
col_names <- "Estimate"
keep <- 1
}
# run tests
test_that("summary has correct structure", {
# summary
expect_s3_class(summary, "summary_test_mediation")
# original output of test for indirect effect
expect_identical(summary$object, boot)
# summary of the model fit
expect_s3_class(summary$summary, "summary_cov_fit_mediation")
expect_s3_class(summary$summary, "summary_fit_mediation")
# no regression model summaries
expect_null(summary$summary$fit_mx)
expect_null(summary$summary$fit_ymx)
})
test_that("attributes are correctly passed through summary", {
# robustness
if (method == "winsorized") {
expect_true(summary$summary$robust)
} else {
expect_false(summary$summary$robust)
}
# number of observations
expect_identical(summary$summary$n, as.integer(n))
# variable names
expect_identical(summary$summary$x, x)
expect_identical(summary$summary$y, y)
expect_identical(summary$summary$m, m)
expect_identical(summary$summary$covariates, covariates)
})
test_that("effect summaries have correct names", {
# a path
expect_identical(dim(summary$summary$a), c(1L, n_col))
expect_identical(rownames(summary$summary$a), x)
expect_identical(colnames(summary$summary$a)[keep], col_names)
# b path
expect_identical(dim(summary$summary$b), c(1L, n_col))
expect_identical(rownames(summary$summary$b), m)
expect_identical(colnames(summary$summary$b)[keep], col_names)
# total effect
expect_identical(dim(summary$summary$total), c(1L, n_col))
expect_identical(rownames(summary$summary$total), x)
expect_identical(colnames(summary$summary$total)[keep], col_names)
# direct effect
expect_identical(dim(summary$summary$direct), c(1L, n_col))
expect_identical(rownames(summary$summary$direct), x)
expect_identical(colnames(summary$summary$direct)[keep], col_names)
})
test_that("effect summaries contain correct coefficient values", {
# effects computed on original sample
expect_identical(summary$summary$a[x, 1], boot$fit$a)
expect_identical(summary$summary$b[m, 1], boot$fit$b)
expect_identical(summary$summary$total[x, 1], boot$fit$total)
expect_identical(summary$summary$direct[x, 1], boot$fit$direct)
# bootstrapped effects
if (type == "boot") {
expect_equal(summary$summary$a[x, "Boot"], boot$a)
expect_equal(summary$summary$b[m, "Boot"], boot$b)
expect_equal(summary$summary$total[x, "Boot"], boot$total)
expect_equal(summary$summary$direct[x, "Boot"], boot$direct)
}
})
}
# loop over settings for retest()
for (setting in c("wider", "less", "greater")) {
# use retest() to change one of the arguments
if (setting == "wider") reboot <- retest(boot, level = level[2])
else reboot <- retest(boot, alternative = setting, level = level[2])
test_that("output of retest() has correct structure", {
# bootstrap test
expect_identical(class(reboot), class(boot))
# regression fit
expect_identical(reboot$fit, boot$fit)
# bootstrap replicates
expect_identical(reboot$reps, boot$reps)
})
test_that("arguments of retest() are correctly passed", {
# alternative hypothesis
if (setting == "wider") {
expect_identical(reboot$alternative, boot$alternative)
} else {
expect_identical(reboot$alternative, setting)
}
# confidence level
expect_identical(reboot$level, level[2])
# type of confidence intervals
expect_identical(reboot$type, boot$type)
})
test_that("retest() yields correct values", {
# indirect effect
expect_identical(reboot$indirect, boot$indirect)
# confidence interval
expect_identical(length(reboot$ci), length(boot$ci))
expect_identical(names(reboot$ci), names(boot$ci))
if (setting == "wider") {
expect_lt(reboot$ci[1], boot$ci[1])
expect_gt(reboot$ci[2], boot$ci[2])
expect_equal(colnames(confint(reboot)), c("2.5 %", "97.5 %"))
} else {
if (setting == "less") {
expect_equivalent(reboot$ci[1], -Inf)
expect_equal(reboot$ci[2], boot$ci[2])
} else {
expect_equal(reboot$ci[1], boot$ci[1])
expect_equivalent(reboot$ci[2], Inf)
}
expect_identical(colnames(confint(reboot)), c("Lower", "Upper"))
}
})
}
# tests for weight_plot(), which is only implemented for ROBMED
test_that("setup_weight_plot() gives error", {
# plot is not implemented
expect_error(setup_weight_plot(boot))
})
# tests for ellipse_plot()
test_that("setup_ellipse_plot() behaves as expected", {
# plot is inherited from model fit
expect_identical(setup_ellipse_plot(boot), setup_ellipse_plot(boot$fit))
})
# loop over settings for ci_plot()
for (setting in c("default", "p_value")) {
# obtain setup object for CI plot
if (setting == "default") ci <- setup_ci_plot(boot)
else ci <- setup_ci_plot(boot, p_value = TRUE)
# run tests
test_that("object returned by setup_ci_plot() has correct structure", {
# check data frame for confidence interval
expect_s3_class(ci$ci, "data.frame")
# check dimensions and column names
if (setting == "default") {
expect_identical(dim(ci$ci), c(2L, 4L))
expect_named(ci$ci, c("Effect", "Estimate", "Lower", "Upper"))
} else {
expect_identical(dim(ci$ci), c(2L, 5L))
expect_named(ci$ci, c("Label", "Effect", "Estimate", "Lower", "Upper"))
}
# check data frame for p-value
if (setting == "default") {
# p-value not plotted
expect_null(ci$p_value)
} else {
# check data frame
expect_s3_class(ci$p_value, "data.frame")
# check dimensions and column names
expect_identical(dim(ci$p_value), c(2L, 3L))
expect_named(ci$p_value, c("Label", "Effect", "Value"))
# check that labels are correct
labels <- c("Confidence interval", "p-Value")
expect_identical(ci$ci$Label,
factor(rep.int(labels[1], 2), levels = labels))
expect_identical(ci$p_value$Label,
factor(rep.int(labels[2], 2), levels = labels))
}
# check that direct effect and indirect effect are plotted by default
names <- c("Direct", "Indirect")
factor <- factor(names, levels = names)
expect_identical(ci$ci$Effect, factor)
# check confidence level
expect_identical(ci$level, level[1])
# check logical for multiple methods
expect_false(ci$have_methods)
})
}
# tests for density_plot()
# obtain setup object for ellipse plot
density <- setup_density_plot(boot)
# run tests
test_that("object returned by setup_density_plot() has correct structure", {
# check data frame for confidence interval
expect_s3_class(density$density, "data.frame")
# check dimensions
expect_identical(ncol(density$density), 2L)
expect_gt(nrow(density$density), 0L)
# check column names
expect_named(density$density, c("Indirect", "Density"))
# check data frame confidence interval
expect_s3_class(density$ci, "data.frame")
# check dimensions
expect_identical(dim(density$ci), c(1L, 3L))
# check column names
expect_named(density$ci, c("Estimate", "Lower", "Upper"))
# check type of test
expect_identical(density$test, "boot")
# check confidence level
expect_identical(density$level, level[1])
# check logical for multiple effects
expect_false(density$have_effect)
# check logical for multiple methods
expect_false(density$have_methods)
})
}
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context("bootstrap test: covariance fit")
## load package
library("robmed", quietly = TRUE)
## control parameters
n <- 250 # number of observations
a <- c <- 0.2 # true effects
b <- 0 # true effect
seed <- 20150601 # seed for the random number generator
## set seed for reproducibility
set.seed(seed)
## generate data
X <- rnorm(n)
M <- a * X + rnorm(n)
Y <- b * M + c * X + rnorm(n)
test_data <- data.frame(X, Y, M)
## arguments for bootstrap tests
x <- "X" # independent variable
y <- "Y" # dependent variable
m <- "M" # mediator variable
covariates <- character() # control variables
R <- 100 # number of bootstrap samples
level <- c(0.9, 0.95) # confidence level
ci_type <- "perc" # type of confidence intervals
ctrl <- cov_control(prob = 0.9) # for winsorized covariance matrix
## perform bootstrap tests
boot_list <- list(
winsorized = {
set.seed(seed)
test_mediation(test_data, x = x, y = y, m = m, test = "boot", R = R,
level = level[1], type = ci_type, method = "covariance",
robust = TRUE, control = ctrl)
},
ML = {
set.seed(seed)
test_mediation(test_data, x = x, y = y, m = m, test = "boot", R = R,
level = level[1], type = ci_type, method = "covariance",
robust = FALSE)
}
)
## correct values
effect_names <- c("a", "b", "Total", "Direct", "Indirect")
## run tests
# loop over methods
methods <- names(boot_list)
for (method in methods) {
# extract information for current method
boot <- boot_list[[method]]
# compute summaries
summary_list <- list(boot = summary(boot, type = "boot"),
data = summary(boot, type = "data"))
## run tests
test_that("output has correct structure", {
# bootstrap test
expect_s3_class(boot, "boot_test_mediation")
expect_s3_class(boot, "test_mediation")
# model fit
expect_s3_class(boot$fit, "cov_fit_mediation")
expect_s3_class(boot$fit, "fit_mediation")
# bootstrap replicates
expect_s3_class(boot$reps, "boot")
})
test_that("arguments are correctly passed", {
# alternative hypothesis
expect_identical(boot$alternative, "twosided")
# number of bootstrap replicates
# (doesn't hold for some methods if there are computational issues)
expect_identical(boot$R, as.integer(R))
# confidence level
expect_identical(boot$level, level[1])
# type of confidence intervals
expect_identical(boot$type, ci_type)
# variable names
expect_identical(boot$fit$x, x)
expect_identical(boot$fit$y, y)
expect_identical(boot$fit$m, m)
expect_identical(boot$fit$covariates, covariates)
# robust or nonrobust fit and test
if (method == "winsorized") {
expect_true(boot$fit$robust)
expect_equal(boot$fit$control, ctrl)
} else {
expect_false(boot$fit$robust)
expect_null(boot$fit$control)
}
})
test_that("dimensions are correct", {
# effects are scalars
expect_length(boot$indirect, 1L)
# only one indirect effect, so only one confidence interval
expect_length(boot$ci, 2L)
expect_named(boot$ci, c("Lower", "Upper"))
# dimensions of bootstrap replicates
expect_identical(dim(boot$reps$t), c(as.integer(R), 5L))
})
test_that("values of coefficients are correct", {
expect_equivalent(boot$a, mean(boot$reps$t[, 1]))
expect_equivalent(boot$b, mean(boot$reps$t[, 2]))
expect_null(boot[["d"]])
expect_equivalent(boot$direct, mean(boot$reps$t[, 4]))
expect_equivalent(boot$indirect, mean(boot$reps$t[, 5]))
expect_equivalent(boot$reps$t[, 5], boot$reps$t[, 1] * boot$reps$t[, 2])
# total effect
expect_equivalent(boot$total, mean(boot$reps$t[, 3]))
expect_equivalent(boot$reps$t[, 3], rowSums(boot$reps$t[, 4:5]))
})
# loop over types of estimation (bootstrap or on original data)
types <- names(summary_list)
for (type in types) {
# run tests
test_that("output of coef() method has correct attributes", {
# extract coefficients
coef <- coef(boot, type = type)
# tests
expect_length(coef, 5L)
expect_named(coef, effect_names)
})
test_that("coef() method returns correct values of coefficients", {
# object containing the true values
true <- if (type == "boot") boot else boot$fit
# tests
expect_equivalent(coef(boot, parm = "a", type = type),
true$a)
expect_equivalent(coef(boot, parm = "b", type = type),
true$b)
expect_null(coef(boot, parm = "d", type = type))
expect_equivalent(coef(boot, parm = "total", type = type),
true$total)
expect_equivalent(coef(boot, parm = "direct", type = type),
true$direct)
expect_equivalent(coef(boot, parm = "indirect", type = type),
true$indirect)
})
test_that("output of confint() method has correct attributes", {
# extract confidence intervals
ci <- confint(boot, type = type)
# tests
expect_equal(dim(ci), c(5L, 2L))
expect_equal(rownames(ci), effect_names)
expect_equal(colnames(ci), c("5 %", "95 %"))
})
test_that("confint() method returns correct values of confidence intervals", {
# confidence interval of indirect effect
ci_indirect <- confint(boot, parm = "indirect", type = type)
expect_equivalent(ci_indirect, boot$ci)
# extract confidence intervals for other effects
keep <- c("a", "b", "total", "direct")
ci_other <- confint(boot, parm = keep, type = type)
coef_other <- coef(boot, parm = keep, type = type)
expect_equivalent(rowMeans(ci_other), coef_other)
})
test_that("output of p_value() method has correct attributes", {
# extract p-value
digits <- 3
p_val <- p_value(boot, type = type, digits = digits)
# tests
expect_length(p_val, 5L)
expect_named(p_val, effect_names)
expect_equal(p_val["Indirect"], round(p_val["Indirect"], digits = digits))
})
# extract current summary
summary <- summary_list[[type]]
# correct values
if (type == "boot") {
n_col <- 5L
col_names <- c("Data", "Boot")
keep <- 1:2
} else {
n_col <- 4L
col_names <- "Estimate"
keep <- 1
}
# run tests
test_that("summary has correct structure", {
# summary
expect_s3_class(summary, "summary_test_mediation")
# original output of test for indirect effect
expect_identical(summary$object, boot)
# summary of the model fit
expect_s3_class(summary$summary, "summary_cov_fit_mediation")
expect_s3_class(summary$summary, "summary_fit_mediation")
# no regression model summaries
expect_null(summary$summary$fit_mx)
expect_null(summary$summary$fit_ymx)
})
test_that("attributes are correctly passed through summary", {
# robustness
if (method == "winsorized") {
expect_true(summary$summary$robust)
} else {
expect_false(summary$summary$robust)
}
# number of observations
expect_identical(summary$summary$n, as.integer(n))
# variable names
expect_identical(summary$summary$x, x)
expect_identical(summary$summary$y, y)
expect_identical(summary$summary$m, m)
expect_identical(summary$summary$covariates, covariates)
})
test_that("effect summaries have correct names", {
# a path
expect_identical(dim(summary$summary$a), c(1L, n_col))
expect_identical(rownames(summary$summary$a), x)
expect_identical(colnames(summary$summary$a)[keep], col_names)
# b path
expect_identical(dim(summary$summary$b), c(1L, n_col))
expect_identical(rownames(summary$summary$b), m)
expect_identical(colnames(summary$summary$b)[keep], col_names)
# total effect
expect_identical(dim(summary$summary$total), c(1L, n_col))
expect_identical(rownames(summary$summary$total), x)
expect_identical(colnames(summary$summary$total)[keep], col_names)
# direct effect
expect_identical(dim(summary$summary$direct), c(1L, n_col))
expect_identical(rownames(summary$summary$direct), x)
expect_identical(colnames(summary$summary$direct)[keep], col_names)
})
test_that("effect summaries contain correct coefficient values", {
# effects computed on original sample
expect_identical(summary$summary$a[x, 1], boot$fit$a)
expect_identical(summary$summary$b[m, 1], boot$fit$b)
expect_identical(summary$summary$total[x, 1], boot$fit$total)
expect_identical(summary$summary$direct[x, 1], boot$fit$direct)
# bootstrapped effects
if (type == "boot") {
expect_equal(summary$summary$a[x, "Boot"], boot$a)
expect_equal(summary$summary$b[m, "Boot"], boot$b)
expect_equal(summary$summary$total[x, "Boot"], boot$total)
expect_equal(summary$summary$direct[x, "Boot"], boot$direct)
}
})
}
# loop over settings for retest()
for (setting in c("wider", "less", "greater")) {
# use retest() to change one of the arguments
if (setting == "wider") reboot <- retest(boot, level = level[2])
else reboot <- retest(boot, alternative = setting, level = level[2])
test_that("output of retest() has correct structure", {
# bootstrap test
expect_identical(class(reboot), class(boot))
# regression fit
expect_identical(reboot$fit, boot$fit)
# bootstrap replicates
expect_identical(reboot$reps, boot$reps)
})
test_that("arguments of retest() are correctly passed", {
# alternative hypothesis
if (setting == "wider") {
expect_identical(reboot$alternative, boot$alternative)
} else {
expect_identical(reboot$alternative, setting)
}
# confidence level
expect_identical(reboot$level, level[2])
# type of confidence intervals
expect_identical(reboot$type, boot$type)
})
test_that("retest() yields correct values", {
# indirect effect
expect_identical(reboot$indirect, boot$indirect)
# confidence interval
expect_identical(length(reboot$ci), length(boot$ci))
expect_identical(names(reboot$ci), names(boot$ci))
if (setting == "wider") {
expect_lt(reboot$ci[1], boot$ci[1])
expect_gt(reboot$ci[2], boot$ci[2])
expect_equal(colnames(confint(reboot)), c("2.5 %", "97.5 %"))
} else {
if (setting == "less") {
expect_equivalent(reboot$ci[1], -Inf)
expect_equal(reboot$ci[2], boot$ci[2])
} else {
expect_equal(reboot$ci[1], boot$ci[1])
expect_equivalent(reboot$ci[2], Inf)
}
expect_identical(colnames(confint(reboot)), c("Lower", "Upper"))
}
})
}
# tests for weight_plot(), which is only implemented for ROBMED
test_that("setup_weight_plot() gives error", {
# plot is not implemented
expect_error(setup_weight_plot(boot))
})
# tests for ellipse_plot()
test_that("setup_ellipse_plot() behaves as expected", {
# plot is inherited from model fit
expect_identical(setup_ellipse_plot(boot), setup_ellipse_plot(boot$fit))
})
# loop over settings for ci_plot()
for (setting in c("default", "p_value")) {
# obtain setup object for CI plot
if (setting == "default") ci <- setup_ci_plot(boot)
else ci <- setup_ci_plot(boot, p_value = TRUE)
# run tests
test_that("object returned by setup_ci_plot() has correct structure", {
# check data frame for confidence interval
expect_s3_class(ci$ci, "data.frame")
# check dimensions and column names
if (setting == "default") {
expect_identical(dim(ci$ci), c(2L, 4L))
expect_named(ci$ci, c("Effect", "Estimate", "Lower", "Upper"))
} else {
expect_identical(dim(ci$ci), c(2L, 5L))
expect_named(ci$ci, c("Label", "Effect", "Estimate", "Lower", "Upper"))
}
# check data frame for p-value
if (setting == "default") {
# p-value not plotted
expect_null(ci$p_value)
} else {
# check data frame
expect_s3_class(ci$p_value, "data.frame")
# check dimensions and column names
expect_identical(dim(ci$p_value), c(2L, 3L))
expect_named(ci$p_value, c("Label", "Effect", "Value"))
# check that labels are correct
labels <- c("Confidence interval", "p-Value")
expect_identical(ci$ci$Label,
factor(rep.int(labels[1], 2), levels = labels))
expect_identical(ci$p_value$Label,
factor(rep.int(labels[2], 2), levels = labels))
}
# check that direct effect and indirect effect are plotted by default
names <- c("Direct", "Indirect")
factor <- factor(names, levels = names)
expect_identical(ci$ci$Effect, factor)
# check confidence level
expect_identical(ci$level, level[1])
# check logical for multiple methods
expect_false(ci$have_methods)
})
}
# tests for density_plot()
# obtain setup object for ellipse plot
density <- setup_density_plot(boot)
# run tests
test_that("object returned by setup_density_plot() has correct structure", {
# check data frame for confidence interval
expect_s3_class(density$density, "data.frame")
# check dimensions
expect_identical(ncol(density$density), 2L)
expect_gt(nrow(density$density), 0L)
# check column names
expect_named(density$density, c("Indirect", "Density"))
# check data frame confidence interval
expect_s3_class(density$ci, "data.frame")
# check dimensions
expect_identical(dim(density$ci), c(1L, 3L))
# check column names
expect_named(density$ci, c("Estimate", "Lower", "Upper"))
# check type of test
expect_identical(density$test, "boot")
# check confidence level
expect_identical(density$level, level[1])
# check logical for multiple effects
expect_false(density$have_effect)
# check logical for multiple methods
expect_false(density$have_methods)
})
}
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