partial_qrf: Partial Dependence Plot for Quantile Random Forest

In vlyubchich/MML: Misc Machine Learning

Partial Dependence Plot for Quantile Random Forest

Description

Get a data frame for partial dependence plots from a quantile random forest. The plots can be obtained using plotting functions from other packages.

Usage

partial_qrf(object, pred.var, Q = c(0.05, 0.5, 0.95), ...)

Arguments

object	a ranger quantile random forest object.
pred.var	character string giving the names of the predictor variables of interest (see partial).
Q	a numeric vector of probabilities for which the plot is desired.
...	other arguments passed to partial.

Value

A data.frame with the following columns: the predictor variables supplied in pred.var, response variable (the name is extracted from the ranger function call), and, if length(Q) > 1, one more column named "Quantile" (for an appropriate order of labels in the plots, Quantile is a factor).

See Also

partial, ranger

Examples

## Not run: 
# Example 1: Swiss
library(dplyr)
library(ggplot2)

# fit a quantile random forest
qrf <- ranger::ranger(Examination ~ ., data = swiss, quantreg = TRUE, num.trees = 50)

# 1.1) a plot for one quantile
partial_qrf(qrf, pred.var = "Agriculture", Q = 0.5) %>%
    ggplot(aes(Agriculture, Examination)) +
        geom_line()

# 1.2) a plot for several quantiles, with decorations
library(hrbrthemes)
library(viridis)
partial_qrf(qrf, pred.var = "Agriculture") %>%
   ggplot(aes(Agriculture, Examination, group = Quantile, color = Quantile)) +
       geom_line() +
       scale_color_viridis(discrete = TRUE) +
       theme_ipsum()

# 1.3) a plot for one quantile for 2 predictors (takes longer, so
# save as an object, can also redefine pred.grid or decrease grid.resolution)
df <- partial_qrf(qrf, pred.var = c("Agriculture", "Catholic"), Q = 0.5,
    grid.resolution = 20)
ggplot(df, aes(Agriculture, Catholic)) +
    geom_tile(aes(fill = Examination)) +
    scale_fill_viridis() +
    theme_ipsum() +
    labs(fill = "Median\nexamination")


# 1.4) a plot for each variable
# define a vector of variable names and desired quantiles
varnames <- qrf$forest$independent.variable.names
qs <- c(0.01, 0.025, 0.05)
# save outputs in a list
ddf <- lapply(varnames, function(vn) partial_qrf(qrf, pred.var = vn, Q = qs))
# use your preferred plotting functions, e.g., ggplot2 with patchwork
# for a common y-axis, get ranges
yrange <- range(sapply(ddf, function(x) range(x[,2])))
# then create a list of ggplots
plist <- lapply(seq_along(varnames), function(vi) {
    ggplot(ddf[[vi]], aes_string(x = varnames[vi], y = "Examination",
        group = "Quantile", color = "Quantile")) +
        geom_line() +
        scale_color_viridis(discrete = TRUE) +
        theme_ipsum(plot_margin = ggplot2::margin(10, 10, 10, 10)) +
        ylim(yrange[1], yrange[2]) + theme(axis.title.y = element_blank())
})
# then organize the plots
library(patchwork)
pl <- wrap_plots(plist) + plot_layout(guides = "collect")
pl
# can stop here or continue grouping
gpl <- patchwork::patchworkGrob(pl)
gridExtra::grid.arrange(gpl, left = "Examination")

## End(Not run)

vlyubchich/MML documentation built on April 16, 2023, 2:02 a.m.