plot_correlations {skpr} | R Documentation |
Plots design diagnostics
Description
Plots design diagnostics
Usage
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. |
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"))