plot {movieROC}

Plot an ROC curve

Description

This is one of the core functions of the movieROC package. It displays the empirical ROC curve estimate from an object of class ‘groc’, ‘hroc’, ‘biroc’ , or ‘multiroc’.

Usage

## S3 method for class 'groc'
plot(x, xlim = c(0, 1), ylim = c(0, 1), lwd = 3, 
  xlab = "False-Positive Rate", ylab = "True-Positive Rate", main = "ROC curve", 
  cex.lab = 1.25, cex.main = 1.5, type = NULL, new = TRUE, ...)
## S3 method for class 'hroc'
plot(x, type = 'S', xlim = c(0,1), ylim = c(0,1), 
  lwd = 3, xlab = "False-Positive Rate", ylab = "True-Positive Rate", 
  main = "ROC Curve", cex.lab = 1.25, cex.main = 1.5, new = TRUE, ...)
## S3 method for class 'biroc'
plot(x, ...)
## S3 method for class 'multiroc'
plot(x, ...)

Arguments

x

An ROC curve object from movieROC package. Possible classes are: ‘groc’ (output of gROC function), ‘hroc’ (output of hROC function), ‘biroc’ (output of biROC function), and ‘multiroc’ (output of multiROC function).

xlim, ylim

Range for x- and y-axis. Default: unit interval.

lwd

Line width of the ROC curve. Default: 3.

xlab, ylab

Label for x- and y-axis.

main

Title for the plot.

cex.lab, cex.main

The magnification to be used for labels and main title, respectively, relative to the current setting of cex. Default: cex.lab = 1.25, cex.main = 1.5.

type

What type of plot should be drawn (see help from plot function in base package). Default: type = "s" (stair steps), except for x object from gROC_param function, in which case type = "l" (lines).

new

If TRUE, a new plot is displayed; otherwise, the ROC curve is plotted over the existing graphic. Default: TRUE.

...

Other graphical parameters to be passed.

Value

A plot of the ROC curve with the selected graphical parameters

Examples

data(HCC)
# ROC curve estimates for gene 03515901 and response tumor
rroc <- gROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Right-sided
lroc <- gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided
hroc <- hROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Transformed by a cubic polinomial

plot(rroc, lty = 2, frame = FALSE)
plot(lroc, new = FALSE)
plot(hroc, new = FALSE, col = "blue")
legend("topleft", legend = c("Right-sided", "Left-sided", "Transformed marker"),
    col = c("black", "black", "blue"), lty = c(1,2,1), lwd = 2, bty = "n")

# ROC curve estimate for genes 20202438 and 18384097 to simultaneously identify tumor
# by a logistic regression model with quadratic formula
biroc <- biROC(X = cbind(HCC$cg20202438, HCC$cg18384097), D = HCC$tumor)
plot(biroc)
legend("bottomright", paste("AUC = ", format(biroc$auc, digits = 3)))

# ROC curve estimate for genes 20202438, 18384097 and 03515901 to simultaneously 
# identify tumor by a linear combinations with fixed parameters by Pepe and Thompson (2000)
multiroc <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901), 
    D = HCC$tumor, method = "fixedLinear", methodLinear = "PepeThompson")
plot(multiroc)
legend("bottomright", paste("AUC = ", format(multiroc$auc, digits = 3)))