dsm (version 2.1.3)

dsm.var.gam: Variance estimation via Bayesian results

Description

Use results from the Bayesian interpretation of the GAM to obtain uncertainty estimates. See Wood (2006).

Usage

dsm.var.gam(dsm.obj, pred.data, off.set = NULL,
    seglen.varname = "Effort", type.pred = "response")

Arguments

dsm.obj

an object returned from running dsm.

pred.data

either: a single prediction grid or list of prediction grids. Each grid should be a data.frame with the same columns as the original data.

off.set

a a vector or list of vectors with as many elements as there are in pred.data. Each vector is as long as the number of rows in the corresponding element of pred.data. These give the area associated with each prediction po

seglen.varname

name for the column which holds the segment length (default value "Effort").

type.pred

should the predictions be on the "response" or "link" scale? (default "response").

Value

• a list with elements ll{model the fitted model object pred.var covariances of the regions given in pred.data. Diagonal elements are the variances in order bootstrap logical, always FALSE pred.data as above off.set as above model the fitted model with the extra term dsm.object the original model, as above }

Details

NB. We include uncertainty in the detection function using the delta method so INDEPENDENCE is still assumed between the two variance components

This is based on dsm.var.prop by Mark Bravington and Sharon Hedley.