| .rcspline.plot {CalibrationCurves} | R Documentation |
Internal function
Description
Adjusted version of the rcspline.plot function where only the output is returned and no plot is made
Usage
.rcspline.plot(
x,
y,
model = c("logistic", "cox", "ols"),
xrange,
event,
nk = 5,
knots = NULL,
show = c("xbeta", "prob"),
adj = NULL,
xlab,
ylab,
ylim,
plim = c(0, 1),
plotcl = TRUE,
showknots = TRUE,
add = FALSE,
plot = TRUE,
subset,
lty = 1,
noprint = FALSE,
m,
smooth = FALSE,
bass = 1,
main = "auto",
statloc
)
Arguments
x |
a numeric predictor |
y |
a numeric response. For binary logistic regression, |
model |
|
xrange |
range for evaluating |
event |
event/censoring indicator if |
nk |
number of knots |
knots |
knot locations, default based on quantiles of |
show |
|
adj |
optional matrix of adjustment variables |
xlab |
|
ylab |
|
ylim |
|
plim |
|
plotcl |
plot confidence limits |
showknots |
show knot locations with arrows |
add |
add this plot to an already existing plot |
plot |
logical to indicate whether a plot has to be made. |
subset |
subset of observations to process, e.g. |
lty |
line type for plotting estimated spline function |
noprint |
suppress printing regression coefficients and standard errors |
m |
for |
smooth |
plot nonparametric estimate if |
bass |
smoothing parameter (see |
main |
main title, default is |
statloc |
location of summary statistics. Default positioning by clicking left mouse button where upper left corner of statistics should appear.
Alternative is |
Value
list with components (‘knots’, ‘x’, ‘xbeta’, ‘lower’, ‘upper’) which are respectively the knot locations, design matrix, linear predictor, and lower and upper confidence limits
See Also
lrm, cph, rcspline.eval, plot, supsmu,
coxph.fit, lrm.fit