plot_correlations: Plots design diagnostics

View source: R/plot_correlations.R

plot_correlationsR Documentation

Plots design diagnostics

Description

Plots design diagnostics

Usage

plot_correlations(
  genoutput,
  model = NULL,
  customcolors = NULL,
  pow = 2,
  custompar = NULL
)

Arguments

genoutput

The output of either gen_design or eval_design/eval_design_mc

model

Default 'NULL'. Defaults to the model used in generating/evaluating the design, augmented with 2-factor interactions. If specified, it will override the default model used to generate/evaluate the design.

customcolors

A vector of colors for customizing the appearance of the colormap

pow

Default 2. The interaction level that the correlation map is showing.

custompar

Default NULL. Custom parameters to pass to the 'par' function for base R plotting.

Value

Silently returns the correlation matrix with the proper row and column names.

Examples

#We can pass either the output of gen_design or eval_design to plot_correlations
#in order to obtain the correlation map. Passing the output of eval_design is useful
#if you want to plot the correlation map from an externally generated design.

#First generate the design:

candidatelist = expand.grid(cost = c(15000, 20000), year = c("2001", "2002", "2003", "2004"),
                           type = c("SUV", "Sedan", "Hybrid"))
cardesign = gen_design(candidatelist, ~(cost+type+year)^2, 30)
plot_correlations(cardesign)

#We can also increase the level of interactions that are shown by default.

plot_correlations(cardesign, pow = 3)

#You can also pass in a custom color map.
plot_correlations(cardesign, customcolors = c("blue", "grey", "red"))
plot_correlations(cardesign, customcolors = c("blue", "green", "yellow", "orange", "red"))

skpr documentation built on April 9, 2022, 1:06 a.m.