cgam (version 1.4)

plotpersp: Create a 3D Plot for a CGAM or WPS Object

Description

Given an object of the cgam class, which has at least two non-parametrically modelled predictors, this routine will make a 3D plot of the fit with a set of two non-parametrically modelled predictors in the formula being the x and y labs. If there are more than two non-parametrically modelled predictors, any other such predictor will be evaluated at the largest value which is smaller than or equal to its median value.

If there is any categorical covariate and if the user specifies the argument categ to be a character representing a categorical covariate in the formula, then a 3D plot with multiple parallel surfaces, which represent the levels of a categorical covariate in an ascending order, will be created; otherwise, a 3D plot with only one surface will be created. Each level of a categorical covariate will be evaluated at its mode.

This routine is extended to make a 3D plot for an object of the wps class, which has only two isotonically modelled predictors. See the documentation below for more details.

This routine is an extension of the generic R graphics routine persp.

Usage

plotpersp(object, x1, x2,...)

Arguments

object

An object of the cgam class with at least two non-parametrically modelled predictors, or an object of the wps class.

x1

A non-parametrically modelled predictor in a cgam or wps fit.

x2

A non-parametrically modelled predictor in a cgam or wps fit.

...

Arguments to be passed to the S3 method for the cgam or wps class:

• data:

{The data frame based on which the user get a cgam or wps fit.} surface:{The type of the surface of a 3D plot. For a cgam fit, if surface == "mu", the

Value

• The routine plotpersp returns a 3D plot of an object of the cgam class or the wps class. The $x$ lab and $y$ lab represent a set of non-parametrically modelled predictors used in a cgam formula and represent the two isotonically modelled predictors in a wps formula. For a cgam fit, the $z$ lab represents the estimated mean value or the estimated systematic component value, and for a wps fit, the $z$ lab represents the constrained or the unconstrained estimated mean value.

References

The official documentation for the generic R routine persp: http://stat.ethz.ch/R-manual/R-patched/library/graphics/html/persp.html

Examples

# Example 1.
  data(FEV)

  # extract the variables
  y <- FEV$FEV
  age <- FEV$age
  height <- FEV$height
  sex <- FEV$sex
  smoke <- FEV$smoke

  fit <- cgam(y ~ incr(age) + incr(height) + factor(sex) + factor(smoke), nsim = 0)
  fit.s <- cgam(y ~ s.incr(age) + s.incr(height) + factor(sex) + factor(smoke), nsim = 0)

  plotpersp(fit, age, height, x_grid = 10, y_grid = 10, main = "Cgam Increasing Fit", 
  sub = "Categorical Variable: Sex", categ = "factor(sex)")
  plotpersp(fit.s, age, height, x_grid = 10, y_grid = 10, main = "Cgam Smooth Increasing Fit", 
  sub = "Categorical Variable: Smoke", categ = "factor(smoke)")

# Example 2.
  data(plasma)

  # extract the variables
  y <- plasma$logplasma
  bmi <- plasma$bmi
  dietfat <- plasma$dietfat
  cholest <- plasma$cholest
  fiber <- plasma$fiber
  betacaro <- plasma$betacaro
  retinol <- plasma$retinol
  smoke <- plasma$smoke
  vituse <- plasma$vituse

  fit <- cgam(y ~  s.decr(bmi) + s.decr(dietfat) + s.decr(cholest) + s.incr(fiber) 
+ s.incr(betacaro) + s.incr(retinol) + factor(smoke) + factor(vituse)) 

  plotpersp(fit, bmi, dietfat, x_grid = 10, y_grid = 10, th = 120, ylab = "log(dietfat)", 
zlab = "est mean of log(plasma)", main = "Cgam Fit with the Plasma Data Set", 
sub = "Categorical Variable: Vitamin Use", categ = "factor(vituse)")

# Example 3.
  data(plasma)
  addl <- 1:314*0 + 1 
  addl[runif(314) < .3] <- 2
  addl[runif(314) > .8] <- 4
  addl[runif(314) > .8] <- 3

  ans <- cgam(logplasma ~ s.incr(betacaro, 5) + s.decr(bmi) + s.decr(dietfat) 
+ as.factor(addl), data = plasma)
  plotpersp(ans, betacaro, dietfat, th = 240, random = TRUE, 
categ = "as.factor(addl)", data = plasma)

# Example 4.
  n <- 100
  set.seed(123)
  x1 <- sort(runif(n))
  x2 <- sort(runif(n))
  y <- 4 * (x1 - x2) + rnorm(n, sd = .5)

  # regress y on x1 and x2 under the shape-restriction: "decreasing-increasing"
  # with a penalty term = .1
  ans <- wps(y ~ di(x1, x2), pen = .1)

# plot the constrained surface
  plotpersp(ans, x1, x2, surface = "C")
# plot the unconstrained surface
  plotpersp(ans, x1, x2, surface = "U", th = 120)