io.glm: Iterative offset GLM/GAM for fitting detection function

Description

Provides an iterative algorithm for finding the MLEs of detection (capture) probabilities for a two-occasion (double observer) mark-recapture experiment using standard algorithms GLM/GAM and an offset to compensate for conditioning on the set of observations. While the likelihood can be formulated and solved numerically, the use of GLM/GAM provides all of the available tools for fitting, predictions, plotting etc without any further development.

Usage

io.glm(
  datavec,
  fitformula,
  eps = 1e-05,
  iterlimit = 500,
  GAM = FALSE,
  gamplot = TRUE
)

Value

list of class("ioglm","glm","lm") or class("ioglm","gam")

glmobj: GLM or GAM object
offsetvalue: offsetvalues from iterative fit
plotobj: gam plot object (if GAM & gamplot==TRUE, else NULL)

Arguments

datavec: dataframe
fitformula: logit link formula
eps: convergence criterion
iterlimit: maximum number of iterations allowed
GAM: uses GAM instead of GLM for fitting
gamplot: set to TRUE to get a gam plot object if GAM=TRUE

Author

Jeff Laake, David Borchers, Charles Paxton

Details

Note that currently the code in this function for GAMs has been commented out until the remainder of the mrds package will work with GAMs. This is an internal function that is used as by ddf.io.fi to fit mark-recapture models with 2 occasions. The argument mrmodel is used for fitformula.

References

Buckland, S.T., J.M. breiwick, K.L. Cattanach, and J.L. Laake. 1993. Estimated population size of the California gray whale. Marine Mammal Science, 9:235-249.

Burnham, K.P., S.T. Buckland, J.L. Laake, D.L. Borchers, T.A. Marques, J.R.B. Bishop, and L. Thomas. 2004. Further topics in distance sampling. pp: 360-363. In: Advanced Distance Sampling, eds. S.T. Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.