pdpPairs

Creates a pairs plot showing bivariate pdp on upper diagonal, ice/univariate pdp on the diagonal and data on the lower diagonal

pdpPairs(
  data,
  fit,
  response,
  vars = NULL,
  pal = rev(RColorBrewer::brewer.pal(11, "RdYlBu")),
  fitlims = "pdp",
  gridSize = 10,
  nmax = 500,
  class = 1,
  nIce = 30,
  colorVar = NULL,
  comboImage = FALSE,
  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.
vars	The variables to plot (and their order), defaults to all variables other than response.
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.
nIce	Number of ice curves to be plotted, defaults to 30.
colorVar	Which variable to colour the predictions by.
comboImage	If TRUE draws pdp for mixed variable plots as an image, otherwise an interaction plot.
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.

Value

A pairs plot

Examples

# Load in the data:
aq <- na.omit(airquality)
f <- lm(Ozone ~ ., data = aq)
pdpPairs(aq, f, "Ozone")
#> Generating ice/pdp fits... waiting...
#> Finished ice/pdp

# \donttest{
# Run a ranger model:
library(ranger)
library(MASS)
Boston1 <- Boston[, c(4:6, 8, 13:14)]
Boston1$chas <- factor(Boston1$chas)
fit <- ranger(medv ~ ., data = Boston1, importance = "permutation")
pdpPairs(Boston1[1:30, ], fit, "medv")
#> Generating ice/pdp fits... waiting...
#> Finished ice/pdp

pdpPairs(Boston1[1:30, ], fit, "medv", comboImage = TRUE)
#> Generating ice/pdp fits... waiting...
#> Finished ice/pdp

viv <- vivi(Boston1, fit, "medv")
#> Agnostic variable importance method used.
#> Calculating interactions...
# show top variables only
pdpPairs(Boston1[1:30, ], fit, "medv", comboImage = TRUE, vars = rownames(viv)[1:4])
#> Generating ice/pdp fits... waiting...
#> Finished ice/pdp

# }
# \donttest{
library(ranger)
rf <- ranger(Species ~ ., data = iris, probability = TRUE)
pdpPairs(iris, rf, "Species") # prediction probs for first class, setosa
#> Generating ice/pdp fits... waiting...
#> Finished ice/pdp

pdpPairs(iris, rf, "Species", class = "versicolor") # prediction probs versicolor
#> Generating ice/pdp fits... waiting...
#> Finished ice/pdp

# }