negexp.like | R Documentation |
Computes the negative exponential distance function.
negexp.like(a, dist, covars)
a |
A vector or matrix of covariate
and expansion term
coefficients. Dimension is $k$ X $p$, where
$k$ (i.e., |
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 |
The negative exponential likelihood is
f(x|a) = \exp(-ax)
where a
is the
slope parameter.
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
.
dfuncEstim
,
hazrate.like
,
negexp.like
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")
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