mpt (version 0.6-2)

# plot.mpt: Diagnostic Plot for MPT Models

## Description

Plots MPT residuals against fitted values.

## Usage

```# 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"), …)```

## 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.

## Value

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

## Details

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

## See Also

`mpt`, `residuals.glm`.

## Examples

```# 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
# }
```