Learn R Programming

DSpat (version 0.1.6)

integrate.intensity: Integrated intensity of fitted model

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

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

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.

See Also

lgcp.correction