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

Description Usage Arguments Details Value Author(s) See Also Examples

Description

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

Usage

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  ## 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

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  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 May 30, 2017, 2:10 a.m.

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