pdpZen: Create a zenplot displaying partial dependence values.

In vivid: Variable Importance and Variable Interaction Displays

Create a zenplot displaying partial dependence values.

Description

Constructs a zigzag expanded navigation plot (zenplot) displaying partial dependence values.

Usage

pdpZen(
  data,
  fit,
  response,
  zpath = NULL,
  pal = rev(RColorBrewer::brewer.pal(11, "RdYlBu")),
  fitlims = "pdp",
  gridSize = 10,
  nmax = 500,
  class = 1,
  comboImage = FALSE,
  rug = TRUE,
  predictFun = NULL,
  convexHull = FALSE,
  probability = FALSE,
  ...
)

Arguments

data	Data frame used for fit
fit	A supervised machine learning model, which understands condvis2::CVpredict
response	The name of the response for the fit
zpath	Plot shows consecutive pairs of these variables. Defaults to all variables other than response. Recommend constructing zpath witn calcZpath.
pal	A vector of colors to show predictions, for use with scale_fill_gradientn
fitlims	Specifies the fit range for the color map. Options are a numeric vector of length 2, "pdp" (default), in which cases limits are calculated from the pdp, or "all", when limits are calculated from the observations and pdp predictions outside fitlims are squished on the color scale.
gridSize	The size of the grid for evaluating the predictions.
nmax	Uses sample of nmax data rows for the pdp. Default is 500. Use all rows if NULL.
class	Category for classification, a factor level, or a number indicating which factor level.
comboImage	If TRUE draws pdp for mixed variable plots as an image, otherwise an interaction plot.
rug	If TRUE adds rugs for the data to the pdp plots
predictFun	Function of (fit, data) to extract numeric predictions from fit. Uses condvis2::CVpredict by default, which works for many fit classes.
convexHull	If TRUE, then the convex hull is computed and any points outside the convex hull are removed.
probability	if TRUE, then returns the partial dependence for classification on the probability scale. If FALSE (default), then the partial dependence is returned on a near logit scale.
...	passed on to zenplot

Value

A zenplot of partial dependence values.

Examples

## Not run: 
# To use this function, install zenplots and graph from Bioconductor.
if (!requireNamespace("graph", quietly = TRUE)) {
  install.packages("BiocManager")
  BiocManager::install("graph")
}
install.packages("zenplots")

library(MASS)
library(ranger)
Boston1 <- Boston
Boston1$chas <- factor(Boston1$chas)
rf <- ranger(medv ~ ., data = Boston1)
pdpZen(Boston1[1:30, ], rf, response = "medv", zpath = names(Boston1)[1:4], comboImage = T)
# Find the top variables in rf
set.seed(123)
viv <- vivi(Boston1, rf, "medv", nmax = 30) # use 30 rows, for speed
pdpZen(Boston1, rf, response = "medv", zpath = rownames(viv)[1:4], comboImage = T)
zpath <- zPath(viv, cutoff = .2) # find plots whose interaction score exceeds .2
pdpZen(Boston1, rf, response = "medv", zpath = zpath, comboImage = T)

## End(Not run)

vivid documentation built on July 26, 2023, 5:22 p.m.