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#' @name plotDiag
#'
#' @title Make the diagnostic plots for hierarchical voronoi tessellations model.
#'
#' @param model_obj List. A list of model_obj obtained from the HVT
#' function or prediction object
#' @author Shubhra Prakash <shubhra.prakash@@mu-sigma.com>
#' @seealso \code{\link{plotHVT}}
#' @keywords diagnostics
#' @importFrom magrittr %>%
#' @import ggplot2
#' @export plotDiag
plotDiag <-
function (model_obj){
# browser()
# require(ggplot2)
# require(magrittr)
# require(patchwork)
# Model Print
if(model_obj[["model_info"]][["type"]]=="hvt_model"){
p1=model_obj[[4]]$datapoint_plot+ggplot2::ggtitle("Minimum Intra-DataPoint Distance Plot: Train Data")
p2=model_obj[[4]]$cent_plot+ggplot2::ggtitle("Minimum Intra-Centroid Distance Plot: HVT Model | Train Data")
p3=model_obj[[4]]$mad_plot_train+ggplot2::ggtitle("Mean Absolute Deviation Plot: Calibration: HVT Model | Train Data")
p4=model_obj[[4]]$number_plot+ggplot2::ggtitle("Distribution of Number of Observations in Cells: HVT Model | Train Data")
p5=model_obj[[4]]$singleton_piechart
if (model_obj[["model_info"]][["input_parameters"]][["hvt_validation"]]){
p6=hvt.results[[5]][["mad_plot"]]+ggplot2::ggtitle("Mean Absolute Deviation Plot:Validation")
plotDiag=(p3/(p1|p2)/(p4|p5)/p6)
} else{
plotDiag=(p3/(p1|p2)/(p4|p5))
}
} else if(model_obj[["model_info"]][["type"]]=="hvt_prediction" ){
# browser()
mtrain=model_obj[["model_mad_plots"]][["mtrain"]]+ggplot2::ggtitle("Mean Absolute Deviation Plot: Calibration on Train Data")
mtest=model_obj[["model_mad_plots"]][["mtest"]]+ggplot2::ggtitle("Mean Absolute Deviation Plot:Validation")
mpred=madPlot(model_obj)+ggplot2::ggtitle("Mean Absolute Deviation Plot:Test Data")
#
plotDiag=mtrain/mtest/mpred
}
return(plotDiag)
}
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