rem.glm {mrds}

Iterative offset model fitting of mark-recapture with removal model

Description

Detection function fitting from mark-recapture data with a removal configuration in which a secondary observer knows what the primary observer detects and detects objects missed by the primary observer. The iterative offset glm/gam uses an offset to compensate for the conditioning on the set of objects seen by either observer (eg 00 those missed by both observers are not included in the analysis. This function is similar to io.glm.

Usage

rem.glm(
  datavec,
  fitformula,
  eps = 1e-05,
  iterlimit = 500,
  GAM = FALSE,
  gamplot = TRUE,
  datavec2
)

Arguments

datavec

dataframe containing records seen by either observer 1 or 2

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

datavec2

dataframe containing all records for observer 1 and observer 2 as in io.glm form; this is used in case there is an observer(not platform effect)

Details

The only difference between this function and io.glm is the offset and the data construction because there is only one detection function being estimated for the primary observer. The two functions could be merged.

Value

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

glmobj

GLM or GAM object

offsetvalue

offsetvalues from iterative fit

plotobj

gam plot object (if GAM & gamplot==TRUE, else NULL)

Note

currently the code in this function for GAMs has been commented out until the remainder of the mrds package will work with GAMs.

Author(s)

Jeff Laake

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.