Plotting Partial Dependence Functions

Description

Plots partial dependence functions (i.e., marginal effects) using lattice graphics.

Usage

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plotPartial(x, ...)

## S3 method for class 'partial'
plotPartial(x, smooth = FALSE, rug = FALSE,
  chull = FALSE, levelplot = TRUE, contour = FALSE, number = 4,
  overlap = 0.1, train = NULL, col.regions = viridis::viridis, ...)

Arguments

x

An object that inherits from the "partial" class.

...

Additional optional arguments to be passed onto dotplot, levelplot, xyplot, or wireframe.

smooth

Logical indicating whether or not to overlay a LOESS smooth. Default is FALSE.

rug

Logical indicating whether or not to include rug marks on the predictor axes. Only used when plot = TRUE. Default is FALSE.

chull

Logical indicating wether or not to restrict the first two variables in pred.var to lie within the convex hull of their training values; this affects pred.grid. Default is FALSE.

levelplot

Logical indicating whether or not to use a false color level plot (TRUE) or a 3-D surface (FALSE). Default is TRUE.

contour

Logical indicating whether or not to add contour lines to the level plot. Only used when levelplot = TRUE. Default is FALSE.

number

Integer specifying the number of conditional intervals to use for the continuous panel variables. See ?graphics::co.intervals and ?lattice::equal.count for further details.

overlap

The fraction of overlap of the conditioning variables. See ?graphics::co.intervals and ?lattice::equal.count for further details.

train

Data frame containing the original training data. Only required if rug = TRUE or chull = TRUE.

col.regions

Color vector to be used if levelplot is TRUE. Defaults to the wonderful Matplotlib 'viridis' color map provided by the viridis package. See ?viridis::viridis for details.

Examples

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# See ?partial for examples
?partial