
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
.
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)
a fit object from orm
if TRUE
, ExProb
use the integer codes
ignored for ExProb
. Passed to plot
for
plot.ExProb
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 the first variable is the grouping
variable (corresponding to what made the linear predictor vary) and
the second variable is the data vector for the y
variable.
The rows of data should be sorted to be in order of the linear
predictor argument.
an object created by running the function created by ExProb
limits for x-axis; default is range of observed y
x-axis label
y-axis label
color for horizontal lines and points
color for vertical discontinuities
plotting symbol for predicted curves
line width for predicted curves
plotting parameters for data
set to FALSE
to suppress key in plot if data
is given
ExProb
returns an R function. Running the function returns an
object of class "ExProb"
.
# 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')
# }
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