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#' @title plotDiag
#'
#' Make the diagnostic plots for hierarchical voronoi tessellations model.
#'
#' @param hvt.results List. A list of hvt.results obtained from the HVT
#' function.
#' @param hvt.predictions List. A list of hvt.predictions obtained from the Predict
#' function.
#' @author Shubhra Prakash <shubhra.prakash@@mu-sigma.com>
#' @seealso \code{\link{plotHVT}}
#' @keywords diagnostics
#' @importFrom magrittr %>%
#' @import ggplot2
#' @export qeHistPlot
qeHistPlot=function(hvt.results,hvt.predictions){
# require(patchwork)
val=hvt.results[[3]][["max_QE"]]%>%unlist()%>%as.numeric()
p1=ggplot2::ggplot() +
ggplot2::aes(x = val) +
ggplot2::geom_histogram(fill = "midnightblue",colour="white",alpha=0.75) +
ggplot2::theme_minimal()+
ggplot2::geom_vline(xintercept = mean(val[val>0.00000001]),linetype="dashed")+
ggplot2::geom_vline(xintercept = mean(val),colour="red",linetype="dashed")+ ggplot2::ggtitle("1.Max QE Distribution: Train ")+
ggplot2::xlab("Max QE values for cells: Train Data")
# +
# ggtitle("Max QE Distribution (Train)")
pred_val=hvt.predictions[["scoredPredictedData"]]
p2=ggplot2::ggplot() +
ggplot2::aes(x = pred_val$Quant.Error) +
ggplot2::geom_histogram(fill = "midnightblue",colour="white",alpha=0.75) +
ggplot2::theme_minimal()+
ggplot2::geom_vline(xintercept = mean(pred_val$Quant.Error), colour="red",linetype="dashed")+
ggplot2::ggtitle("2.Max QE Distribution: Test ")+
ggplot2::xlab("Max QE values for cells: Test Data")
plot_output= p1/p2
return(plot_output)
}
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