For an orm object ExProb 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. Survival
generates a similar function but for computing survival probabilities Prob(Y>y)
and adding an origin of zero. Plotting of survival curves is done with a
survplot method. Survival.orm merely calls ExProb.orm
with argument survival=TRUE. For survival estimation when interval
censoring is present, times are taken as interval midpoints with intervals
corresponding to intercepts in the model.
Optionally a normal approximation (normality for the linear predictor) for a confidence
interval for exceedance probabilities will be computed, if
conf.int > 0 is specified to the function generated from calling
ExProb or Survival. For ExProb, a "lims" attribute is included
in the result computed by the derived cumulative probability function. For Survival,
the result is a data frame if conf.int is specified or both time and the requested
linear predictor are varying. In the data frame the limits are variables lower and upper.
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)
# S3 method for orm
Survival(object, ...)
ExProb and Survival return an R function. Running the function returns an
object of class "ExProb" for ExProb, or a data frame or vector for Survival.
a fit object from orm. For Survival the fit may be from orm.fit.
This is used to estimate survival curves when there are no predictors in the model. In the case
the link function (family argument to orm.fit) does not affect survival probabilities
but does affect confidence limits. To get the same confidence intervals as survival:survfit.formula
use ormfit(y=, family='loglog') to correspond to survfit(..., conf.type='log-log').
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
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
Frank Harrell and Shengxin Tu
orm, Quantile.orm