Zelig (version 4.2-1)

rocplot: Receiver Operator Characteristic Plots

Description

The 'rocplot' command generates a receiver operator characteristic plot to compare the in-sample (default) or out-of-sample fit for two logit or probit regressions.

Usage

rocplot(y1, y2, fitted1, fitted2, cutoff = seq(from=0,
    to=1, length=100), lty1="solid", lty2="dashed",
    lwd1=par("lwd"), lwd2=par("lwd"), col1=par("col"),
    col2=par("col"), main="ROC Curve",
    xlab = "Proportion of 1's Correctly Predicted",
    ylab="Proportion of 0's Correctly Predicted",
    plot = TRUE, ... )

Arguments

y1

response variable for the first model

y2

response variable for the second model

fitted1

fitted values for the first model. These values may represent either the in-sample or out-of-sample fitted values

fitted2

fitted values for the second model

cutoff

A vector of cut-off values between 0 and 1, at which to evaluate the proportion of 0s and 1s correctly predicted by the first and second model. By default, this is 100 increments between 0 and 1 inclusive

lty1

the line type of the first model (defaults to 'line')

lty2

the line type of the second model (defaults to 'dashed')

lwd1

the line width of the first model (defaults to 1)

lwd2

the line width of the second model (defaults to 1)

col1

the color of the first model (defaults to 'black')

col2

the color of the second model (defaults to 'black')

main

a title for the plot (defaults to "ROC Curve")

xlab

a label for the X-axis

ylab

a lavel for the Y-axis

plot

whether to generate a plot to the selected device

...

additional parameters to be passed to the plot

Value

• if plot is TRUE, rocplot simply generates a plot. Otherwise, a list with the following is produced:
• roc1
  a matrix containing a vector of x-coordinates and y-coordinates corresponding to the number of ones and zeros correctly predicted for the first model.
• roc2
  a matrix containing a vector of x-coordinates and y-coordinates corresponding to the number of ones and zeros correctly predicted for the second model.
• area1
  the area under the first ROC curve, calculated using Reimann sums.
• area2
  the area under the second ROC curve, calculated using Reimann sums.