patterncausality (version 0.1.3)

plotCV: Plot Cross-Validation Results for Pattern Causality

Description

The plotCV function generates a plot to visualize the results of cross-validation for pattern causality. It displays the positive, negative, and dark causality strengths across different sample sizes, providing a clear graphical representation of the cross-validation outcomes.

Usage

plotCV(pcCV, fr = FALSE)

Value

A plot visualizing the positive, negative, and dark causality strengths across different sample sizes. The plot includes points and lines for each causality type, along with a legend for easy interpretation.

Arguments

pcCV

A data frame containing the cross-validation results from the pcCrossValidation function. The data frame should include columns for positive, negative, and dark causality strengths, along with the corresponding sample sizes.

fr

A bool value for the plot frame

Examples

# \donttest{
data(DJS)
X <- DJS$X3M
Y <- DJS$American.Express
numberset <- c(1000,2000,3000,4000)
result <- pcCrossValidation(X,Y,3,2,"euclidean",1,FALSE,numberset)
plotCV(result,FALSE)
# }