Nothing
context("Residual plots")
test_that("autoplot works for \"clm\" objects", {
# Skips
skip_on_cran()
skip_if_not_installed("ordinal")
# Load data
data(df1)
# Fit cumulative link model
fit <- ordinal::clm(y ~ x + I(x ^ 2), data = df1, link = "logit")
# Construct residual plots
p1 <- ggplot2::autoplot(fit, what = "qq")
p2 <- ggplot2::autoplot(fit, what = "covariate", x = df1$x)
p3 <- ggplot2::autoplot(fit, what = "fitted")
p4 <- ggplot2::autoplot(fit, what = "fitted", nsim = 10)
# Expectations
expect_is(p1, "ggplot")
expect_is(p2, "ggplot")
expect_is(p3, "ggplot")
expect_is(p4, "ggplot")
})
# test_that("autoplot works for \"glm\" objects", {
#
# # Skips
# skip_on_cran()
#
# # Load data
# data(df1)
#
# # Fit cumulative link model
# fit <- glm(y ~ x + I(x ^ 2), data = df1, family = binomial)
#
# # Construct residual plots
# p1 <- ggplot2::autoplot(fit, method = "jitter", what = "qq")
# p2 <- ggplot2::autoplot(fit, method = "jitter", what = "covariate", x = df1$x)
# p3 <- ggplot2::autoplot(fit, method = "jitter", what = "fitted")
# p4 <- ggplot2::autoplot(fit, method = "jitter", what = "fitted", nsim = 10)
#
#
# # Expectations
# expect_error(ggplot2::autoplot(fit, jitter.scale = "response", what = "qq"))
# expect_is(p1, "ggplot")
# expect_is(p2, "ggplot")
# expect_is(p3, "ggplot")
# expect_is(p4, "ggplot")
#
# })
test_that("autoplot works for \"lrm\" objects", {
# Skips
skip_on_cran()
skip_if_not_installed("rms")
# Load data
data(df1)
# Fit cumulative link model
fit <- rms::lrm(y ~ x, data = df1)
# Construct residual plots
p1 <- ggplot2::autoplot(fit, what = "qq")
p2 <- ggplot2::autoplot(fit, what = "covariate", x = df1$x)
p3 <- ggplot2::autoplot(fit, what = "fitted")
p4 <- ggplot2::autoplot(fit, what = "fitted", nsim = 10)
# Expectations
expect_is(p1, "ggplot")
expect_is(p2, "ggplot")
expect_is(p3, "ggplot")
expect_is(p4, "ggplot")
})
test_that("autoplot works for \"orm\" objects", {
# Skips
skip_on_cran()
skip_if_not_installed("rms")
# Load data
data(df1)
# Fit cumulative link model
fit <- rms::orm(y ~ x, data = df1, family = logistic)
# Construct residual plots
p1 <- ggplot2::autoplot(fit, what = "qq")
p2 <- ggplot2::autoplot(fit, what = "covariate", x = df1$x)
p3 <- ggplot2::autoplot(fit, what = "fitted")
p4 <- ggplot2::autoplot(fit, what = "fitted", nsim = 10)
# Expectations
expect_is(p1, "ggplot")
expect_is(p2, "ggplot")
expect_is(p3, "ggplot")
expect_is(p4, "ggplot")
})
test_that("autoplot works for \"polr\" objects", {
# Skips
skip_on_cran()
skip_if_not_installed("MASS")
# Load data
data(df1)
# Fit cumulative link model
fit <- MASS::polr(y ~ x + I(x ^ 2), data = df1, method = "logistic")
# Construct residual plots
p1 <- ggplot2::autoplot(fit, what = "qq")
p2 <- ggplot2::autoplot(fit, what = "covariate", x = df1$x)
p3 <- ggplot2::autoplot(fit, what = "fitted")
p4 <- ggplot2::autoplot(fit, what = "fitted", nsim = 10)
# Expectations
expect_is(p1, "ggplot")
expect_is(p2, "ggplot")
expect_is(p3, "ggplot")
expect_is(p4, "ggplot")
})
test_that("autoplot works for \"vglm\" objects", {
# Skips
skip_on_cran()
skip_if_not_installed("rms")
# Load data
data(df1)
# Fit cumulative link model
suppressWarnings(
fit <- VGAM::vglm(y ~ x + I(x ^ 2), data = df1,
family = VGAM::cumulative(link = "logit",
parallel = TRUE))
)
# Construct residual plots
p1 <- ggplot2::autoplot(fit, what = "qq")
p2 <- ggplot2::autoplot(fit, what = "covariate", x = df1$x)
p3 <- ggplot2::autoplot(fit, what = "fitted")
p4 <- ggplot2::autoplot(fit, what = "fitted", nsim = 10)
# Expectations
expect_is(p1, "ggplot")
expect_is(p2, "ggplot")
expect_is(p3, "ggplot")
expect_is(p4, "ggplot")
})
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