plotROC: plot the ROC curve

In pathwayTMB: Pathway Based Tumor Mutational Burden

plot the ROC curve

Description

This function uses to plot a ROC curve.

Usage

plotROC(
  riskscore,
  response,
  main,
  add = FALSE,
  col = par("col"),
  legacy.axes = TRUE,
  print.auc = FALSE,
  grid = FALSE,
  auc.polygon = FALSE,
  auc.polygon.col = "skyblue",
  max.auc.polygon = FALSE,
  max.auc.polygon.col = "#EEEEEE"
)

Arguments

riskscore	a numeric vector of the same length than response, containing the predicted value of each observation.
response	a factor, numeric or character vector of responses (true class), typically encoded with 0 (controls) and 1 (cases). Only two classes can be used in a ROC curve.
main	the title of the ROC curve
add	if TRUE, the ROC curve will be added to an existing plot. If FALSE (default), a new plot will be created.
col	the color of the ROC curve
legacy.axes	a logical indicating if the specificity axis (x axis) must be plotted as as decreasing “specificity” (FALSE) or increasing “1 - specificity” (TRUE, the default) as in most legacy software. This affects only the axis, not the plot coordinates.
print.auc	boolean. Should the numeric value of AUC be printed on the plot?
grid	boolean or numeric vector of length 1 or 2. Should a background grid be added to the plot? Numeric: show a grid with the specified interval between each line; Logical: show the grid or not. Length 1: same values are taken for horizontal and vertical lines. Length 2: grid value for vertical (grid[1]) and horizontal (grid[2]). Note that these values are used to compute grid.v and grid.h. Therefore if you specify a grid.h and grid.v, it will be ignored.
auc.polygon	boolean. Whether or not to display the area as a polygon.
auc.polygon.col	color (col) for the AUC polygon.
max.auc.polygon	boolean. Whether or not to display the maximal possible area as a polygon.
max.auc.polygon.col	color (col) for the maximum AUC polygon.

Value

No return value

Examples

#get the path of the mutation annotation file and samples' survival data
maf<-system.file("extdata","data_mutations_extended.txt",package = "pathwayTMB")
sur_path<-system.file("extdata","sur.csv",package = "pathwayTMB")
sur<-read.csv(sur_path,header=TRUE,row.names = 1)
#perform the function 'get_mut_matrix'
mut_matrix<-get_mut_matrix(maffile=maf,mut_fre = 0.01,is.TCGA=FALSE,sur=sur)
#perform the function `get_PTMB`
PTMB_matrix<-get_PTMB(freq_matrix=mut_matrix,genesmbol=genesmbol,gene_path=gene_path)
set.seed(1)
final_character<-get_final_signature(PTMB=PTMB_matrix,sur=sur)
#calculate the risksciore
riskscore<-plotKMcurves(t(PTMB_matrix[final_character,]),sur=sur,plots=FALSE)$risk_score
#get the path of samples' immunotherapy response data
res_path<- system.file("extdata","response.csv",package = "pathwayTMB")
response<-read.csv(res_path,header=TRUE,stringsAsFactors =FALSE,row.name=1)
plotROC(riskscore=riskscore,response=response,main="Objective Response",print.auc=TRUE)

pathwayTMB documentation built on Aug. 9, 2022, 5:09 p.m.