| secr.model.density {secr} | R Documentation |
SECR can fit an inhomogeneous Poisson model to describe the distribution of animals. This may be viewed as a surface of expected density across the study area.
The log likelihood is evaluated in secr.fit by summing values at
points on a 'habitat mask'. Each point in a habitat mask represents a
grid cell of potentially occupied habitat (their combined area may be
almost any shape and may include disjunct patches). The full design
matrix for density (D) has one row for each point in the mask. The
design matrix has one column for the intercept (constant) term and one
for each predictor.
Predictors may be based on Cartesian coordinates (e.g. 'x' for an east-west trend), a continuous habitat variable (e.g. vegetation cover) or a categorical (factor) habitat variable. Predictors must be known for all points in the mask (non-habitat excluded). The variables 'x', 'y', 'session' and 'g' are provided automatically. Other covariates should be named columns in the 'covariates' attribute of the habitat mask.
| Variable | Description | Data source |
| x | x-coordinate | automatic |
| y | y-coordinate | automatic |
| session | session factor | automatic |
| g | group factor | automatic |
| [user] | mask covariate | covariates (mask) as named in formula |
The submodel for density (D) is a named component of the list used in
the model argument of secr.fit. It is expressed in R
formula notation by appending terms to ~.
This implementation is still experimental. Note that no density model is
fitted when secr.fit is called with CL = TRUE.
Murray Efford murray.efford@otago.ac.nz
Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.
secr models, secr detection models, secr.fit
list(D = ~ 1) ## constant density (homogeneous Poisson) list(D = ~ x) ## east-west trend list(D = ~ cover) ## requires 'cover' as a mask covariate