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="logistic",xrange,event,nk=5,knots=NULL, show="xbeta",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)
y
should be
0-1
.
"logistic"
or
"cox"
. For
"cox"
, uses the
coxph.fit
with
method="efron"
.
function.
x
, default is
f
and
1-f
quantiles of
x
,
where
f=10/max(n,200)
model="cox"
. If
event
is
present,
model
is assumed to be
"cox"
x
(by
rcspline.eval
)
"xbeta"
or
"prob"
- what is plotted on
y
-axis
x
-axis label, default is
"label"
attribute of
x
y
y
-axis limits for logit or log hazard
y
-axis limits for probability scale
subset=sex=="male"
model="logistic"
, plot grouped estimates with triangles. Each
group contains
m
ordered observations on
x
.
model="logistic"
and
adj
is
not specified
supsmu
)
"Estimated Spline Transformation"
"ll"
to place below the graph on the
lower left, or the actual
x
and
y
coordinates.
Use
"none"
to suppress statistics.
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
# rcspline.plot(cad.dur, tvdlm, m=150) # rcspline.plot(log10(cad.dur+1), tvdlm, m=150)