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context("geom-quantile")
test_that("geom_quantile matches quantile regression", {
set.seed(6531)
x <- rnorm(10)
df <- tibble::tibble(
x = x,
y = x^2 + 0.5 * rnorm(10)
)
ps <- ggplot(df, aes(x, y)) + geom_quantile()
quants <- c(0.25, 0.5, 0.75)
pred_rq <- predict(
quantreg::rq(y ~ x,
tau = quants,
data = df
),
data_frame(
x = seq(min(x), max(x), length.out = 100)
)
)
pred_rq <- cbind(seq(min(x), max(x), length.out = 100), pred_rq)
colnames(pred_rq) <- c("x", paste("Q", quants * 100, sep = "_"))
# pred_rq is a matrix; convert it to data.frame so that it can be compared
pred_rq <- as.data.frame(pred_rq)
ggplot_data <- layer_data(ps)
pred_rq_test_25 <- pred_rq[, c("x", "Q_25")]
colnames(pred_rq_test_25) <- c("x", "y")
# Use expect_equivalent() to ignore rownames
expect_equivalent(
ggplot_data[ggplot_data$quantile == 0.25, c("x", "y")],
pred_rq_test_25
)
pred_rq_test_50 <- pred_rq[, c("x", "Q_50")]
colnames(pred_rq_test_50) <- c("x", "y")
expect_equivalent(
ggplot_data[ggplot_data$quantile == 0.5, c("x", "y")],
pred_rq_test_50
)
pred_rq_test_75 <- pred_rq[, c("x", "Q_75")]
colnames(pred_rq_test_75) <- c("x", "y")
expect_equivalent(
ggplot_data[ggplot_data$quantile == 0.75, c("x", "y")],
pred_rq_test_75
)
})
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