plot_correlations function

Plots design diagnostics

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

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.
• standardize: Default TRUE. Whether to standardize (scale to -1 and 1 and center) the continuous numeric columns. Not standardizing the numeric columns can increase multi-collinearity (predictors that are correlated with other predictors in the model).
• plot: Default TRUE. If FALSE, this will return the correlation matrix.

Returns

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"))