rcspline.plot {Hmisc} | R Documentation |
Provides plots of the estimated restricted cubic spline function relating
a single predictor to the response for a logistic or Cox model.
The rcspline.plot
function does not allow for interactions as do
lrm
and cph
, but it can provide detailed output for
checking spline fits. This function uses the rcspline.eval
,
lrm.fit
, and Therneau's coxph.fit
functions
and plots the estimated spline regression and confidence limits,
placing summary statistics on the graph. If there are no
adjustment variables, rcspline.plot
can also plot two alternative
estimates of the regression function when model="logistic"
:
proportions or logit
proportions on grouped data, and a nonparametric estimate. The
nonparametric regression estimate is based on smoothing the binary
responses and taking the logit transformation of the smoothed
estimates, if desired. The smoothing uses supsmu
.
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,subset,lty=1,noprint=FALSE,m,smooth=FALSE,bass=1, main="auto",statloc)
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 |
same for |
ylim |
|
plim |
|
plotcl |
plot confidence limits |
showknots |
show knot locations with arrows |
add |
add this plot to an already existing plot |
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 e.g. |
statloc |
location of summary statistics. Default positioning by
clicking left mouse button where upper left corner of statistics
should appear. Alternative is |
list with components knots, x, xbeta, lower, upper
which are respectively
the knot locations, design matrix, linear predictor, and lower and upper
confidence limits
Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu
lrm
, cph
, rcspline.eval
, plot
, supsmu
, coxph.fit
, lrm.fit
#rcspline.plot(cad.dur, tvdlm, m=150) #rcspline.plot(log10(cad.dur+1), tvdlm, m=150)