dsm.var.gam: Prediction variance estimation assuming independence

In DistanceDevelopment/dsm: Density Surface Modelling of Distance Sampling Data

Prediction variance estimation assuming independence

Description

This function is deprecated, use dsm_var_gam.

Usage

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

Arguments

dsm.obj	a model object fitted by 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 cell. If a single number is supplied it will be replicated for the length of pred.data.
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").

DistanceDevelopment/dsm documentation built on Dec. 2, 2022, 6:08 a.m.