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