ExProb
Function Generator For Exceedance Probabilities
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 orm
ExProb(object, codes = FALSE, ...)
# S3 method for ExProb
plot(x, …, 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 toplot
forplot.ExProb
- data
Specify
data
if you want to add stratified empirical probabilities to the graph. Ifdata
is a numeric vector, it is assumed that no groups are present. Otherwisedata
must be a list or data frame where the first variable is the grouping variable (corresponding to what made the linear predictor vary) and the second variable is the data vector for they
variable. The rows of data should be sorted to be in order of the linear predictor argument.- 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 ifdata
is given
Value
ExProb
returns an R function. Running the function returns an
object of class "ExProb"
.
See Also
Examples
# NOT RUN {
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')
# }