HVT (version 25.2.4)

plotModelDiagnostics: Make the diagnostic plots for hierarchical voronoi tessellations

Description

This is the main function that generates diagnostic plots for hierarchical voronoi tessellations models and scoring.

Usage

plotModelDiagnostics(model_obj)

Value

For trainHVT, Minimum Intra-DataPoint Distance Plot, Minimum Intra-Centroid Distance Plot Mean Absolute Deviation Plot, Distribution of Number of Observations in Cells, for Training Data and Mean Absolute Deviation Plot for Validation Data are plotted. For scoreHVT Mean Absolute Deviation Plot for Training Data and Validation Data are plotted

Arguments

model_obj

List. A list obtained from the trainHVT function or scoreHVT function

Author

Shubhra Prakash <[e-mail]>

See Also

plotHVT

Examples

data("EuStockMarkets")
hvt.results <- trainHVT(EuStockMarkets, n_cells = 60, depth = 1, quant.err = 0.1, 
                       distance_metric = "L1_Norm", error_metric = "max",
                       normalize = TRUE, quant_method="kmeans", diagnose = TRUE, 
                       hvt_validation = TRUE)
plotModelDiagnostics(hvt.results)