Hmisc (version 5.3-0)

rcspline.plot: 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)

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

Examples

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