rms (version 8.2-1)

bplot: 3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit

Description

Uses lattice graphics and the output from Predict to plot image, contour, or perspective plots showing the simultaneous effects of two continuous predictor variables. Unless formula is provided, the \(x\)-axis is constructed from the first variable listed in the call to Predict and the \(y\)-axis variable comes from the second.

The perimeter function is used to generate the boundary of data to plot when a 3-d plot is made. It finds the area where there are sufficient data to generate believable interaction fits.

Usage

bplot(x, formula, lfun=lattice::levelplot, xlab, ylab, zlab,
      adj.subtitle=!info$ref.zero, cex.adj=.75, cex.lab=1,
      perim, showperim=FALSE,
      zlim=range(yhat, na.rm=TRUE), scales=list(arrows=FALSE),
      xlabrot, ylabrot, zlabrot=90, ...)
perimeter(x, y, xinc=diff(range(x))/10, n=10, lowess.=TRUE)

Value

perimeter returns a matrix of class perimeter. This outline can be conveniently plotted by lines.perimeter.

Details

perimeter is a kind of generalization of datadist for 2 continuous variables. First, the n smallest and largest x values are determined. These form the lowest and highest possible xs to display. Then x is grouped into intervals bounded by these two numbers, with the interval widths defined by xinc. Within each interval, y is sorted and the \(n\)th smallest and largest y are taken as the interval containing sufficient data density to plot interaction surfaces. The interval is ignored when there are insufficient y values. When the data are being readied for persp, bplot uses the approx function to do linear interpolation of the y-boundaries as a function of the x values actually used in forming the grid (the values of the first variable specified to Predict). To make the perimeter smooth, specify lowess.=TRUE to perimeter.

See Also

datadist, Predict, rms, rmsMisc, levelplot, contourplot, wireframe

Examples

n <- 1000    # define sample size
set.seed(17) # so can reproduce the results
age            <- rnorm(n, 50, 10)
blood.pressure <- rnorm(n, 120, 15)
cholesterol    <- rnorm(n, 200, 25)
sex            <- factor(sample(c('female','male'), n,TRUE))
label(age)            <- 'Age'      # label is in Hmisc
label(cholesterol)    <- 'Total Cholesterol'
label(blood.pressure) <- 'Systolic Blood Pressure'
label(sex)            <- 'Sex'
units(cholesterol)    <- 'mg/dl'   # uses units.default in Hmisc
units(blood.pressure) <- 'mmHg'

# Specify population model for log odds that Y=1
L <- .4*(sex=='male') + .045*(age-50) +
  (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))
# Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)]
y <- ifelse(runif(n) < plogis(L), 1, 0)

ddist <- datadist(age, blood.pressure, cholesterol, sex)
options(datadist='ddist')

fit <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)),
               x=TRUE, y=TRUE)
p <- Predict(fit, age, cholesterol, sex, np=50) # vary sex last
require(lattice)
bplot(p)                 # image plot for age, cholesterol with color
                         # coming from yhat; use default ranges for
                         # both continuous predictors; two panels (for sex)
bplot(p, lfun=wireframe) # same as bplot(p,,wireframe)
# View from different angle, change y label orientation accordingly
# Default is z=40, x=-60
bplot(p,, wireframe, screen=list(z=40, x=-75), ylabrot=-25)
bplot(p,, contourplot)   # contour plot
bounds  <- perimeter(age, cholesterol, lowess=TRUE)
plot(age, cholesterol)     # show bivariate data density and perimeter
lines(bounds[,c('x','ymin')]); lines(bounds[,c('x','ymax')])
p <- Predict(fit, age, cholesterol)  # use only one sex
bplot(p, perim=bounds)   # draws image() plot
                         # don't show estimates where data are sparse
                         # doesn't make sense here since vars don't interact
bplot(p, plogis(yhat) ~ age*cholesterol) # Probability scale
options(datadist=NULL)