integrate.intensity: Integrated intensity of fitted model

In DSpat: Spatial Modelling for Distance Sampling Data

Description

Compute intensity and its integration (abundance) and measures of precision with and without over-dispersion correction

Usage

integrate.intensity(x, dimyx=NULL, eps=NULL, se=FALSE, od=FALSE,
                    reps=100, silent=FALSE, J.inv=NULL, showplot=TRUE)

Arguments

x	dspat object
dimyx	number of y,x pixels
eps	height and width of pixels
se	if TRUE, compute std error of abundance and log-normal ci
od	if TRUE and se=TRUE, also compute over-dispersion corrected std error of abundance and log-normal ci
reps	number of reps for MC integration for over-dispersion correction
silent	if FALSE, show progress on MC integration
J.inv	var-cov matrix from fitted model
showplot	if TRUE show Poisson and empirical and fitted K-functions

Details

Either dimyx or eps can be specified. If neither specified then it uses the first covariate image in the dspat object to set the intensity grid. If more than one are specified then others are ignored with their priority for use matching the order they are listed above.

Value

Abundance	Estimate of expected abundance in the study area
distribution	dataframe containing N (predicted number of points in the cell),x,y (x,y coordinates of cell) and covariates used in the model
precision	List containing se, lcl.95, ucl.95, J.inv, and b.vec
precision.od	For over-dispersion estimate a list containing se, lcl.95, ucl.95, J.inv, and b.vec
lambda	estimated intensity image
W	window mask for study area

Author(s)

Devin Johnson; Jeff Laake

See Also

lgcp.correction

DSpat documentation built on May 2, 2019, 11:10 a.m.