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

In aod: Analysis of Overdispersed Data

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 [e-mail], Renaud Lancelot [e-mail]

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)
  

aod documentation built on April 2, 2022, 9:05 a.m.