residuals.overglm: Residuals for alternatives to the Poisson and Binomial...

In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

Residuals for alternatives to the Poisson and Binomial Regression Models under the presence of Overdispersion.

Description

Computes various types of residuals to assess the individual quality of model fit for regression models based on the negative binomial, beta-binomial, and random-clumped binomial distributions, which are alternatives to the Poisson and binomial regression models under the presence of overdispersion.

Usage

## S3 method for class 'overglm'
residuals(
  object,
  type = c("quantile", "standardized", "response"),
  plot.it = FALSE,
  identify,
  ...
)

Arguments

object	an object of class overglm.
type	an (optional) character string which allows to specify the required type of residuals. The available options are: (1) the difference between the observed response and the fitted mean ("response"); (2) the standardized difference between the observed response and the fitted mean ("standardized"); and (3) the randomized quantile residual ("quantile"). By default, type is set to be "quantile".
plot.it	an (optional) logical switch indicating if the plot of residuals versus the fitted values is required. By default, plot.it is set to be FALSE.
identify	an (optional) positive integer value indicating the number of individuals to identify on the plot of residuals versus the fitted values. This is only appropriate if plot.it=TRUE.
...	further arguments passed to or from other methods. If plot.it=TRUE then ... may be used to include graphical parameters to customize the plot. For example, col, pch, cex, main, sub, xlab, ylab.

Value

A vector with the observed type-type residuals.

References

Dunn P.K., Smyth G.K. (1996) Randomized Quantile Residuals. Journal of Computational and Graphical Statistics, 5, 236-244.

Examples

###### Example 1: Self diagnozed ear infections in swimmers
data(swimmers)
fit1 <- overglm(infections ~ frequency + location, family="nb1(log)", data=swimmers)
residuals(fit1, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
          col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 2: Article production by graduate students in biochemistry PhD programs
bioChemists <- pscl::bioChemists
fit2 <- overglm(art ~ fem + kid5 + ment, family="nb1(log)", data = bioChemists)
residuals(fit2, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
          col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 3: Agents to stimulate cellular differentiation
data(cellular)
fit3 <- overglm(cbind(cells,200-cells) ~ tnf + ifn, family="bb(logit)", data=cellular)
residuals(fit3, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
          col.axis="blue", col.main="black", family="mono", cex=0.8)

glmtoolbox documentation built on Oct. 10, 2023, 9:06 a.m.