R/plotDiag.R

In muHVT: Constructing Hierarchical Voronoi Tessellations and Overlay Heatmap for Data Analysis

#' @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)
  }