R: Residuals for Maximum-Likelihood and Quasi-Likelihood Models

residuals-methods {aod} | R Documentation

## Residuals for Maximum-Likelihood and Quasi-Likelihood Models

### Description

Residuals of models fitted with functions `betabin` and `negbin` (formal class “glimML”), or `quasibin` and `quasipois` (formal class “glimQL”).

### Usage

      ## S4 method for signature 'glimML'
    residuals(object, type = c("pearson", "response"), ...)
      ## S4 method for signature 'glimQL'
    residuals(object, type = c("pearson", "response"), ...)

### Arguments

`object` | Fitted model of formal class “glimML” or “glimQL”.
`type` | Character string for the type of residual: “pearson” (default) or “response”.
`...` | Further arguments to be passed to the function, such as `na.action`.

### Details

For models fitted with `betabin` or `quasibin`, Pearson's residuals are computed as:

(y - n * p.fit) / (n * p.fit * (1 - p.fit) * (1 + (n - 1) * φ))^{0.5}

where y and n are respectively the numerator and the denominator of the response, p.fit is the fitted probability and φ is the fitted overdispersion parameter. When n = 0, the residual is set to 0. Response residuals are computed as y/n - p.fit.
For models fitted with `negbin` or `quasipois`, Pearson's residuals are computed as:

(y - y.fit) / (y.fit + φ * y.fit^2)^{0.5}

where y and y.fit are the observed and fitted counts, respectively. Response residuals are computed as y - y.fit.

### Value

A numeric vector of residuals.

### Author(s)

Matthieu Lesnoff matthieu.lesnoff@cirad.fr, Renaud Lancelot renaud.lancelot@cirad.fr

### See Also

`residuals.glm`

### Examples

      data(orob2)
      fm <- betabin(cbind(y, n - y) ~ seed, ~ 1,
                     link = "logit", data = orob2)
      #Pearson's chi-squared goodness-of-fit statistic
      sum(residuals(fm, type = "pearson")^2)

[Package aod version 1.3 Index]