rms (version 4.1-3)

ExProb: Function Generator For Exceedance Probabilities

Description

For an orm object generates a function for computing the estimates of the function Prob(Y>=y) given one or more values of the linear predictor using the reference (median) intercept. This function can optionally be evaluated at only a set of user-specified y values, otherwise a right-step function is returned. There is a plot method for plotting the step functions, and if more than one linear predictor was evaluated multiple step functions are drawn. ExProb is especially useful for nomogram.

Usage

ExProb(object, ...)
## S3 method for class 'orm':
ExProb(object, codes = FALSE, ...)
## S3 method for class 'ExProb':
plot(x, \dots, data=NULL,
                      xlim=NULL, xlab=x$yname, ylab=expression(Prob(Y>=y)),
                      col=par('col'), col.vert='gray85', pch=20,
                      pch.data=21, lwd=par('lwd'), lwd.data=lwd,
                      lty.data=2, key=TRUE)

Arguments

object

a fit object from orm

codes

if TRUE, ExProb use the integer codes $1,2,\ldots,k$ for the $k$-level response instead of its original unique values

...

ignored for ExProb. Passed to plot for plot.ExProb

data

Specify data if you want to add stratified empirical probabilities to the graph. If data is a numeric vector, it is assumed that no groups are present. Otherwise data must be a list or data frame where

x

an object created by running the function created by ExProb

xlim

limits for x-axis; default is range of observed y

xlab

x-axis label

ylab

y-axis label

col

color for horizontal lines and points

col.vert

color for vertical discontinuities

pch

plotting symbol for predicted curves

lwd

line width for predicted curves

pch.data,lwd.data,lty.data

plotting parameters for data

key

set to FALSE to suppress key in plot if data is given

Value

• ExProb returns an R function. Running the function returns an object of class "ExProb".

See Also

orm, Quantile.orm

Examples

set.seed(1)
x1 <- runif(200)
yvar <- x1 + runif(200)
f <- orm(yvar ~ x1)
d <- ExProb(f)
lp <- predict(f, newdata=data.frame(x1=c(.2,.8)))
w <- d(lp)
s1 <- abs(x1 - .2) < .1
s2 <- abs(x1 - .8) < .1
plot(w, data=data.frame(x1=c(rep(.2, sum(s1)), rep(.8, sum(s2))),
                        yvar=c(yvar[s1], yvar[s2])))

qu <- Quantile(f)
abline(h=c(.1,.5), col='gray80')
abline(v=qu(.5, lp), col='gray80')
abline(v=qu(.9, lp), col='green')