dsm.var.gam: Prediction variance estimation assuming independence
In dsm: Density Surface Modelling of Distance Sampling Data

dsm.var.gam

R Documentation

Prediction variance estimation assuming independence

This function is deprecated, use dsm_var_gam.

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

`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"`).