Rdistance (version 4.0.5)

negexp.like: negexp.like - Negative exponential likelihood

Description

Computes the negative exponential distance function.

Usage

negexp.like(a, dist, covars)

Value

A list containing the following two components:

• L.unscaled: A matrix of size $n$X$k$X$b$ containing likelihood values evaluated at distances in dist. Each row is associated with a single distance, and each column is associated with a single case (row of a). This matrix is "unscaled" because the underlying likelihood does not integrate to one. Values in L.unscaled are always greater than or equal to zero.

• params: A $n$X$k$X$b$ array of the likelihood's (canonical) parameters, First page contains parameter values related to covariates (i.e., $s = exp(x'a)$), while subsequent pages contain other parameters. $b$ = 1 for halfnorm, negexp; $b$ = 2 for hazrate and others. Rows correspond to distances in dist. Columns correspond to rows from argument a.

Arguments

a

A vector or matrix of covariate and expansion term coefficients. Dimension is $k$ X $p$, where $k$ (i.e., nrow(a)) is the number of coefficient vectors to evaluate (cases) and $p$ (i.e., ncol(a)) is the number of covariate and expansion coefficients in the likelihood. If a is a dimensionless vector, it is interpreted to be a single row with $k$ = 1. Covariate coefficients in a are the first $q$ values ($q$ <= $p$), and must be on a log scale.

dist

A numeric vector of length $n$ or a single-column matrix (dimension $n$X1) containing detection distances at which to evaluate the likelihood.

covars

A numeric vector of length $q$ or matrix of dimension $n$X$q$ containing covariate values associated with distances in argument d

Details

The negative exponential likelihood is $$f(x|a) = \exp(-ax)$$ where \(a\) is the slope parameter.

See Also

dfuncEstim, hazrate.like, negexp.like

Examples

d <- seq(0, 100, length=100)
covs <- matrix(1,length(d),1)
negexp.like(log(0.01), d, covs)

# Changing slope parameter
plot(d, negexp.like(log(0.1), d, covs)$L.unscaled, type="l", col="red")
lines(d, negexp.like(log(0.05), d, covs)$L.unscaled, col="blue")