splinePlot.lrm: Plot predictions of logistic regression

In tagteam/Publish: Format Output of Various Routines in a Suitable Way for Reports and Publication

Plot predictions of logistic regression

Description

Plotting the prediction of a logistic regression model with confidence bands against one continuous variable.

Usage

splinePlot.lrm(
  object,
  xvar,
  xvalues,
  xlim = range(xvalues),
  ylim,
  xlab = xvar,
  ylab = scale[[1]],
  col = 1,
  lty = 1,
  lwd = 3,
  confint = TRUE,
  newdata = NULL,
  scale = c("risk", "odds"),
  add = FALSE,
  ...
)

Arguments

object	Logistic regression model fitted with rms::lrm
xvar	Name of the variable to show on x-axis
xvalues	Sequence of xvar values
xlim	x-axis limits
ylim	y-axis limits
xlab	x-axis labels
ylab	y-axis labels
col	color of the line
lty	line style
lwd	line width
confint	Logical. If TRUE show confidence shadows
newdata	How to adjust
scale	Character string that determines the outcome scale (y-axis). Choose between "risk" and "odds".
add	Logical. If TRUE add lines to an existing graph
...	Further arguments passed to plot. Only if add is FALSE.

Details

Function which extracts from a logistic regression model fitted with rms::lrm the predicted risks or odds.

Author(s)

Thomas A. Gerds <[e-mail]>

Examples

data(Diabetes)
Diabetes$hypertension=  1*(Diabetes$bp.1s>140)
library(rms)
uu <- datadist(Diabetes)
options(datadist="uu")
fit=lrm(hypertension~rcs(age)+gender+hdl,data=Diabetes)
splinePlot.lrm(fit,xvar="age",xvalues=seq(30,50,1))

tagteam/Publish documentation built on April 13, 2025, 6:22 a.m.