Rdistance (version 4.3.0)

integrateDfuncs: Integration of distance functions

Description

Integrates under distances functions using exact integrals when possible. If exact integrals are not known, numerical integration is used.

Usage

integrateDfuncs(object, ml)

Value

A vector of areas under the distance functions represented in object. If object is a distance function and newdata is specified, the returned vector's length is nrow(newdata). If object is a distance function and newdata is NULL, returned vector's length is length(distances(object)). If object is a matrix, return's length is nrow(object).

Arguments

object

Either an Rdistance fitted distance function (an object that inherits from class "dfunc"; usually produced by a call to dfuncEstim), or a matrix of canonical distance function parameters (e.g., matrix(exp(fit$par),1)). If a matrix, each row corresponds to a distance function and each column is a parameter. The first column is the parameter related to sighting covariates and must be transformed to the "real" space (i.e., inverse link, which is \(exp()\), must be applied outside this routine). If object is a matrix, it should not have measurement units because only derived quantities (e.g., ESW) have units; Rdistance function parameters themselves never have units.

ml

Either a Rdistance 'model frame' or an Rdistance 'fitted object'. Both are of class "dfunc". Rdistance 'model frames' are lists containing components necessary to estimate a distance function, but no estimates. Rdistance 'model frames' are typically produced by calls to parseModel. Rdistance 'fitted objects' are typically produced by calls to dfuncEstim. 'Fitted objects' are 'model frames' with additional components such as the parameters estimates, log likelihood value, convergence information, and the variance- covariance matrix of the parameters.

Details

Let K be the integral under distance function g(x) (i.e., the output from this routine). In distance analysis, the observation likelihood being evaluated for maximization is the density, f(x) = g(x)/K. K is a key quantity in distance analysis and is called the "effective sampling distance".

Examples

# Faking a model frame
ml <- list( likelihood = "halfnorm"
          , expansions = 0
          , w.lo = 0 %m% .
          , w.hi = 100 %m% .
          , Units = "m"
          )
attr(ml, "transType") <- "line"

parms <- matrix(75, nrow = 1) 
integrateDfuncs(parms, ml)

# check: Normal, 0 to 100, sd = 75, scaled to mode = 1
(pnorm(q = 100, mean = 0, sd = 75) - 0.5) * sqrt(2*pi)*75