dplot3_varimp: Interactive Variable Importance Plot

In egenn/rtemis: Machine Learning and Visualization

Interactive Variable Importance Plot

Description

Plot variable importance using plotly

Usage

dplot3_varimp(
  x,
  names = NULL,
  main = NULL,
  xlab = "Variable Importance",
  ylab = "",
  plot.top = 1,
  labelify = TRUE,
  col = NULL,
  alpha = 1,
  palette = NULL,
  mar = NULL,
  font.size = 16,
  axis.font.size = 14,
  theme = rtTheme,
  showlegend = TRUE,
  ...
)

Arguments

x	Vector, numeric: Input
names	Vector, string: Names of features
main	Character: main title
xlab	Character: x-axis label
ylab	Character: y-axis label
plot.top	Float or Integer: If <= 1, plot this percent highest absolute values, otherwise plot this many top values. i.e.: plot.top = .2 will print the top 20% highest values, and plot.top = 20 will plot the top 20 highest values
labelify	Logical: If TRUE convert names(x) using labelify. Default = TRUE
col	Vector, colors: Single value, or multiple values to define bar (feature) color(s)
alpha	Numeric: Transparency
palette	Character: Name of rtemis palette to use.
mar	Vector, numeric, length 4: Plot margins in pixels (NOT inches). Default = c(50, 110, 50, 50)
font.size	Integer: Overall font size to use (essentially for the title at this point). Default = 14
axis.font.size	Integer: Font size to use for axis labels and tick labels (Seems not to be in same scale as font.size for some reason. Experiment!)
theme	Output of an rtemis theme function (list of parameters) or theme name. Use themes() to print available themes.
showlegend	Logical: If TRUE, show legend
...	Additional arguments passed to theme

Details

A simple plotly wrapper to plot horizontal barplots, sorted by value, which can be used to visualize variable importance, model coefficients, etc.

Author(s)

E.D. Gennatas

Examples

# made-up data
x <- rnorm(10)
names(x) <- paste0("Feature_", seq(x))
dplot3_varimp(x)

egenn/rtemis documentation built on Oct. 28, 2024, 6:30 a.m.