worstErrors: Accuracy of a Quasi-variance Approximation

In DavidFirth/qvcalc: Quasi Variances for Factor Effects in Statistical Models

Accuracy of a Quasi-variance Approximation

Description

Computes the worst relative error, among all contrasts, for the standard error as derived from a set of quasi variances. For details of the method see Menezes (1999) or Firth and Menezes (2004).

Usage

worstErrors(qv.object)

Arguments

qv.object

An object of class qv

Value

A numeric vector of length 2, the worst negative relative error and the worst positive relative error.

Author(s)

David Firth, [e-mail]

References

Firth, D. and Mezezes, R. X. de (2004) Quasi-variances. Biometrika 91, 69–80. c("\Sexpr[results=rd]tools:::Rd_expr_doi(\"#1\")", "10.1093/biomet/91.1.65")\Sexpr{tools:::Rd_expr_doi("10.1093/biomet/91.1.65")}

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.

Menezes, R. X. (1999) More useful standard errors for group and factor effects in generalized linear models. D.Phil. Thesis, Department of Statistics, University of Oxford.

See Also

qvcalc

Examples

##  Overdispersed Poisson loglinear model for ship damage data
##  from McCullagh and Nelder (1989), Sec 6.3.2
library(MASS)
data(ships)
ships$year <- as.factor(ships$year)
ships$period <- as.factor(ships$period)
shipmodel <- glm(formula = incidents ~ type + year + period,
    family = quasipoisson, 
    data = ships, subset = (service > 0), offset = log(service))
shiptype.qvs <- qvcalc(shipmodel, "type")
summary(shiptype.qvs, digits = 4)
worstErrors(shiptype.qvs)

DavidFirth/qvcalc documentation built on Jan. 17, 2024, 12:52 p.m.