Plots design diagnostics
Plots design diagnostics
plot_correlations( genoutput, model = NULL, customcolors = NULL, pow = 2, custompar = NULL )
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. |
Silently returns the correlation matrix with the proper row and column names.
#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"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.