Plots MPT residuals against fitted values.

```
# S3 method for mpt
plot(x, showNames = TRUE,
xlab = "Predicted response probabilities", ylab = "Deviance residuals",
…)
```# S3 method for mpt
residuals(object, type = c("deviance", "pearson"), …)

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.

For `residuals`

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

The deviance residuals are plotted against the predicted response
probabilities. If `showNames`

is true, plotting symbols are the
names of the residuals.

# NOT RUN { ## 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 # }