Nothing
# Load Friedman benchmark data
friedman1 <- gen_friedman(seed = 101)
friedman2 <- gen_friedman(seed = 101, n_bins = 2)
# Fit an additive linear regression model
fit_lm <- lm(y ~ ., data = friedman1)
# Fit an additive logistic regression model
fit_glm <- glm(y ~ ., data = friedman2, family = "binomial")
# Compute variable importance scores
vi_lm <- vi_model(fit_lm)
vi_glm <- vi_model(fit_glm)
# Expectations for `vi_model()`
expect_identical(
current = vi_lm$Importance,
target = unname(abs(summary(fit_lm)$coefficients[, "t value"])[-1])
)
expect_identical(
current = vi_glm$Importance,
target = unname(abs(summary(fit_glm)$coefficients[, "z value"])[-1])
)
# Expectations for `get_feature_names()`
expect_identical(
current = vip:::get_feature_names.lm(fit_lm),
target = paste0("x", 1L:10L)
)
# Setting `type = "raw"` should return the aboldute value of the original
# coefficients (as opposed to |t-value| or |z-value|)
expect_identical(
current = vi_model(fit_lm, type = "raw")$Importance,
target = unname(abs(coef(fit_lm))[-1])
)
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