# Load Friedman benchmark data
friedman1 <- readRDS("friedman.rds")$friedman1
# Add an obvious outlier
friedman1$x.1[1L] <- -999
# Default grid (which includes min/max)
grid1 <- pdp:::pred_grid(
train = friedman1,
pred.var = "x.1"
)
# Trim outliers first
grid2 <- pdp:::pred_grid(
train = friedman1,
pred.var = "x.1",
grid.resolution = 10,
trim.outliers = TRUE
)
# Use quantiles instead
grid3 <- pdp:::pred_grid(
train = friedman1,
pred.var = "x.1",
quantiles = TRUE,
probs = 1:10/10
)
# Expectations
expect_true(
min(grid1$x.1) == -999
)
expect_true(
min(grid2$x.1) >= 0
)
expect_true(
min(grid3$x.1) >= 0
)
expect_error(
pdp:::pred_grid(list("x.1" = 1:10), pred.var = "x.1")
)
expect_error(
pdp:::pred_grid(friedman1, pred.var = "x.1", grid.resolution = 10,
quantiles = TRUE)
)
expect_error(
pdp:::pred_grid(friedman1, pred.var = "x.1", trim.outliers = TRUE,
quantiles = TRUE)
)
df1 <- data.frame(x = c(2, 1, 3, 3), y = c(1, 1, 2, 1))
df2 <- data.frame(x = c(1, 2, 3, 3), y = c(1, 1, 1, 2))
expect_equal(
current = pdp:::order_grid.data.frame(df1),
target = df2,
check.attributes = FALSE
)
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