# Tests for package party (S4 methods)
if (require(MASS, quietly = TRUE)) {
# Load Friedman benchmark data
friedman2 <- readRDS("friedman.rds")$friedman2 # classification (binary)
# Linear discriminant analysis; MASS::lda() ----------------------------------
# Fit model(s)
fit_lda <- lda(y ~ . ^ 2, data = friedman2)
# Partial dependence for x.3
pd_lda <- partial(fit_lda, pred.var = "x.3")
pd_lda_prob <- partial(fit_lda, pred.var = "x.3", prob = TRUE)
# ICE curves for x.3
ice_lda <- partial(fit_lda, pred.var = "x.3", ice = TRUE, center = TRUE)
ice_lda_prob <- partial(fit_lda, pred.var = "x.3", prob = TRUE,
ice = TRUE, center = TRUE)
# Expectation(s)
expect_true(inherits(pd_lda, what = "partial"))
expect_true(inherits(pd_lda_prob, what = "partial"))
expect_true(inherits(ice_lda, what = "cice"))
expect_true(inherits(ice_lda_prob, what = "cice"))
# Display plots in a grid
grid.arrange(
plotPartial(pd_lda),
plotPartial(pd_lda_prob),
plotPartial(ice_lda),
plotPartial(ice_lda_prob),
nrow = 2
)
# Quadratic discriminant analysis; MASS::qda() -------------------------------
# Fit model(s)
fit_qda <- qda(y ~ ., data = friedman2)
# Partial dependence for x.3
pd_qda <- partial(fit_qda, pred.var = "x.3")
pd_qda_prob <- partial(fit_qda, pred.var = "x.3", prob = TRUE)
# ICE curves for x.3
ice_qda <- partial(fit_qda, pred.var = "x.3", ice = TRUE, center = TRUE)
ice_qda_prob <- partial(fit_qda, pred.var = "x.3", prob = TRUE,
ice = TRUE, center = TRUE)
# Expectation(s)
expect_true(inherits(pd_qda, what = "partial"))
expect_true(inherits(pd_qda_prob, what = "partial"))
expect_true(inherits(ice_qda, what = "cice"))
expect_true(inherits(ice_qda_prob, what = "cice"))
# Display plots in a grid
grid.arrange(
plotPartial(pd_qda),
plotPartial(pd_qda_prob),
plotPartial(ice_qda),
plotPartial(ice_qda_prob),
nrow = 2
)
}
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