Fletcher.chat: Estimate overdispersion

View source: R/Fletcher.chat.R

Fletcher.chatR Documentation

Estimate overdispersion

Description

General function for estimating a variance inflation factor (\hat c) from observed counts.

Usage


Fletcher.chat (observed, expected, np, verbose = TRUE, 
    type = c('Fletcher', 'Wedderburn', 'both'), multinomial = FALSE)

Arguments

observed

integer vector of observed counts, or a list of such vectors

expected

numeric vector of expected counts

np

integer number of parameters estimated

verbose

logical; if TRUE returns extended output

type

character

multinomial

logical; if TRUE, one df is subtracted for the constraint

Details

Fletcher.chat applies the overdispersion formula of Fletcher (2012) or computes the conventional (Wedderburn 1974) variance inflation factor X^2/df. It is used by chat.nk and adjustVarD. The inputs ‘observed’ and ‘expected’ are vectors of counts (e.g., number of distinct individuals per detector); ‘observed’ may also be a list of such vectors, possibly simulated.

Value

Output depends on ‘verbose’, ‘observed’ and ‘type’:

– if ‘observed’ is a list of nk vectors (usually generated by simulation) then the output is a vector of (Fletcher or Wedderburn) \hat c values, one element for each component of ‘observed’, unless type = "both" when the output is a list of two such vectors. Argument ‘verbose’ is ignored.

– if ‘observed’ is a simple vector then ‘verbose’ output is a list comprising input values, various summary statistics, and the computed Fletcher overdispersion (‘chat’). The statistic ‘cX2’ is the conventional variance inflation factor of Wedderburn (1974) – X^2/df. For verbose = FALSE, a single estimate of \hat c is returned when type = "Fletcher" or type = "Wedderburn", otherwise a vector of the two estimates.

References

Fletcher, D. (2012) Estimating overdispersion when fitting a generalized linear model to sparse data. Biometrika 99, 230–237.

Wedderburn, R. W. M. (1974) Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika 61, 439–47.

See Also

chat.nk, adjustVarD


secr documentation built on Oct. 18, 2023, 1:07 a.m.