pdpZen {vivid}

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)