radiant.model (version 1.6.12)

plot.nn: Plot method for the nn function

Description

Plot method for the nn function

Usage

# S3 method for nn
plot(
  x,
  plots = "vip",
  size = 12,
  pad_x = 0.9,
  nrobs = -1,
  incl = NULL,
  incl_int = NULL,
  hline = TRUE,
  pdp_range = c(0.025, 0.975),
  minq = NULL,
  maxq = NULL,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from nn

plots

Plots to produce for the specified Neural Network model. Use "" to avoid showing any plots (default). Options are "olden" or "garson" for importance plots, or "net" to depict the network structure

size

Font size used

pad_x

Padding for explanatory variable labels in the network plot. Default value is 0.9, smaller numbers (e.g., 0.5) increase the amount of padding

nrobs

Number of data points to show in dashboard scatter plots (-1 for all)

incl

Which variables to include in a coefficient plot or PDP plot

incl_int

Which interactions to investigate in PDP plots

hline

Add a dashed horizontal line at the mean response (TRUE/FALSE or a numeric value)

pdp_range

Numeric vector c(lo, hi) giving the percentile range for PDP/Prediction plot x-axes (default c(0.025, 0.975))

minq

Deprecated. Use pdp_range[1] instead

maxq

Deprecated. Use pdp_range[2] instead

shiny

Did the function call originate inside a shiny app

custom

Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org for options.

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/model/nn.html for an example in Radiant

See Also

nn to generate results

summary.nn to summarize results

predict.nn for prediction

Examples

result <- nn(titanic, "survived", c("pclass", "sex"), lev = "Yes")
plot(result, plots = "net")
plot(result, plots = "olden")