.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