plot.mpt: Diagnostic Plot for MPT Models

In mpt: Multinomial Processing Tree Models

Diagnostic Plot for MPT Models

Description

Plots MPT residuals against fitted values.

Usage

## S3 method for class 'mpt'
plot(x, showNames = TRUE,
     xlab = "Predicted response probabilities", ylab = "Deviance residuals",
     ...)

## S3 method for class 'mpt'
residuals(object, type = c("deviance", "pearson"), ...)

Arguments

x, object	an object of class mpt, typically the result of a call to mpt.
showNames	logical. Should the names of the residuals be plotted? Defaults to TRUE.
xlab, ylab	graphical parameters passed to plot.
type	the type of residuals which should be returned; the alternatives are: "deviance" (default) and "pearson".
...	further arguments passed to or from other methods.

Details

The deviance residuals are plotted against the predicted response probabilities. If showNames is true, plotting symbols are the names of the residuals.

Value

For residuals, a named vector of residuals having as many elements as response categories.

See Also

mpt, residuals.glm.

Examples

## Compare two constrained MPT models
data(proact)

spec <- mptspec(
  p1*q1*r1,
  p1*q1*(1 - r1),
  p1*(1 - q1)*r1,
  (1 - p1) + p1*(1 - q1)*(1 - r1),

  p2*q2*r2,
  p2*q2*(1 - r2),
  p2*(1 - q2)*r2,
  (1 - p2) + p2*(1 - q2)*(1 - r2),

  p3*q3*r3,
  p3*q3*(1 - r3),
  p3*(1 - q3)*r3,
  (1 - p3) + p3*(1 - q3)*(1 - r3)
)
m1 <- mpt(update(spec, .restr = list(p2=p1, p3=p1)),
          proact[proact$test == 1, ])
m2 <- mpt(update(spec, .restr = list(q2=q1, q3=q1)), m1$y)

par(mfrow = c(1, 2))                 # residuals versus fitted values
plot(m1, main = "p constrained", ylim = c(-3, 3.5))  # good fit
plot(m2, main = "q constrained", ylim = c(-3, 3.5))  # bad fit

sum( resid(m1)^2 )                   # likelihood ratio G2
sum( resid(m1, "pearson")^2 )        # Pearson X2

mpt documentation built on March 23, 2022, 9:06 a.m.