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)
y
should
be either 0 or 1.
"logistic"
or "cox"
. For "cox"
, uses the
coxph.fit
function with method="efron"
arguement set.
x
, default is f and
$1 - \var{f}$ quantiles of x
, where
$\var{f} = 10/max(\var{n}, 200)$
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 the label attribute of
x
y
-axis label, default is the label attribute of
y
y
-axis limits for logit or log hazard
y
-axis limits for probability scale
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.
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)
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