R/plotSimulDRM.R

In clustDRM: Clustering Dose-Response Curves and Fitting Appropriate Models to Them

# TODO: Add comment
# 
# Author: Vahid Nassiri
###############################################################################


#' plot results of the simulation study
#' 
#' a function to make a heatmap of the simulation results for tyhe given measure.
#'  
#' @param simulDRMobj output of simulEvalDRM function
#' @param quantity2Plot single string, the measure which should be plotted. Available choices are: 
#' c("mean", "bias", "mse", "variance", "relativeBias", 
#' 				"absBias", "absRelativeBias")
#' @return a heatmap 
#' @importFrom MCPMod genDFdata 
#' @import pheatmap
#' @import RColorBrewer 
#' @importFrom grDevices colorRampPalette dev.off jpeg postscript
#' @examples 
#' ## gnerating data, a sample of size 20
#' set.seed(11)
#' doses2Use <-  c(0, 5, 20)
#' numRep2Use <- c(3, 3, 3)
#' generatedData <- cbind(rep(1,sum(numRep2Use)),
#' MCPMod::genDFdata("logistic",c(5, 3, 10, 0.05), doses2Use,
#'  numRep2Use, 1), 
#' 		matrix(rnorm(1*sum(numRep2Use)), sum(numRep2Use), 1))
#' colnames(generatedData) <- c("ID", "dose", "response", "x1")
#' for (iGen in 2:20){
#' genData0 <- cbind(rep(iGen,sum(numRep2Use)), 
#' MCPMod::genDFdata("logistic",c(5, 3, 10, 0.05), doses2Use, 
#' numRep2Use, 1), 
#' 			matrix(rnorm(1*sum(numRep2Use)), sum(numRep2Use), 1))
#' 	colnames(genData0) <- c("ID", "dose", "response", "x1")
#' 	generatedData <- rbind(generatedData, genData0)
#' }
#' simRes <- simulEvalDRM (pilotData = 
#' generatedData[generatedData$ID == 2, c(2,3)], 
#' doseLevels = c(0, 4, 20), 
#' 		numReplications = c(6, 3, 3), numSim = 10, 
#' standardDeviation = 1, EDp = 0.5,
#' 		funcList = c("linlog", "emax", "sigEmax", "logistic"))
#' # plot the simulated results
#' plotSimulDRM(simRes, quantity2Plot = "mse")
#' @author Vahid Nassiri and Yimer Wasihun.
#' @export
plotSimulDRM <- function(simulDRMobj, quantity2Plot = c("mean", "bias", "mse", "variance", "relativeBias", 
				"absBias", "absRelativeBias")){
	quantity2Plot <- match.arg(quantity2Plot, c("mean", "bias", "mse", "variance", "relativeBias", 
			"absBias", "absRelativeBias"))
	toPlotObj <- simulDRMobj[4:10]
	names(toPlotObj) <- c("mean", "bias", "mse", "variance", "relativeBias", 
			"absBias", "absRelativeBias")
	pheatmap(t(toPlotObj[[match(quantity2Plot, c("mean", "bias", "mse", "variance", "relativeBias", 
			"absBias", "absRelativeBias"))]]), color = colorRampPalette(rev(brewer.pal(n = 7, name =
											"Spectral")))(1000),
			cluster_rows = FALSE, cluster_cols = FALSE, display_numbers = TRUE)
}