| Fletcher.chat {secr} | R Documentation |
General function for estimating a variance inflation factor (\hat c) from observed counts.
Fletcher.chat (observed, expected, np, verbose = TRUE,
type = c('Fletcher', 'Wedderburn', 'both'), multinomial = FALSE)
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 |
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.
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.
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.