rcspline.plot: Plot Restricted Cubic Spline Function

In Hmisc: Harrell Miscellaneous

Plot Restricted Cubic Spline Function

Description

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.

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,
              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, y should be either 0 or 1.
model	"logistic" or "cox". For "cox", uses the coxph.fit function with method="efron" arguement set.
xrange	range for evaluating x, default is f and 1 - f quantiles of x, where f = \frac{10}{\max{(n, 200)}}
event	event/censoring indicator if model="cox". If event is present, model is assumed to be "cox"
nk	number of knots
knots	knot locations, default based on quantiles of x (by rcspline.eval)
show	"xbeta" or "prob" - what is plotted on ⁠y⁠-axis
adj	optional matrix of adjustment variables
xlab	⁠x⁠-axis label, default is the “label” attribute of x
ylab	⁠y⁠-axis label, default is the “label” attribute of y
ylim	⁠y⁠-axis limits for logit or log hazard
plim	⁠y⁠-axis limits for probability scale
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. sex == "male"
lty	line type for plotting estimated spline function
noprint	suppress printing regression coefficients and standard errors
m	for model="logistic", plot grouped estimates with triangles. Each group contains m ordered observations on x.
smooth	plot nonparametric estimate if model="logistic" and adj is not specified
bass	smoothing parameter (see supsmu)
main	main title, default is "Estimated Spline Transformation"
statloc	location of summary statistics. Default positioning by clicking left mouse button where upper left corner of statistics should appear. Alternative is "ll" to place below the graph on the lower left, or the actual x and y coordinates. Use "none" to suppress statistics.

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

Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
[e-mail]

See Also

lrm, cph, rcspline.eval, plot, supsmu, coxph.fit, lrm.fit

Examples

#rcspline.plot(cad.dur, tvdlm, m=150)
#rcspline.plot(log10(cad.dur+1), tvdlm, m=150)

Hmisc documentation built on June 22, 2024, 12:19 p.m.