negexp.like {Rdistance} | R Documentation |
negexp.like - Negative exponential distance function
Description
Computes the negative exponential form of a distance function
Usage
negexp.like(
a,
dist,
covars = NULL,
w.lo = units::set_units(0, "m"),
w.hi = max(dist),
series = "cosine",
expansions = 0,
scale = TRUE,
pointSurvey = FALSE
)
Arguments
a |
A vector of likelihood parameter values. Length and
meaning depend on |
dist |
A numeric vector containing the observed distances. |
covars |
Data frame containing values of covariates at each
observation in |
w.lo |
Scalar value of the lowest observable distance. This is the left truncation of sighting distances in |
w.hi |
Scalar value of the largest observable distance. This is the right truncation of sighting distances in |
series |
A string specifying the type of expansion to use. Currently, valid values are 'simple', 'hermite', and 'cosine'; but, see
|
expansions |
A scalar specifying the number of terms in |
scale |
Logical scalar indicating whether or not to scale the likelihood so it integrates to 1. This parameter is used to stop recursion in other functions.
If |
pointSurvey |
Boolean. TRUE if |
Details
The negative exponential likelihood is
where is a
slope parameter to be estimated.
Expansion Terms: If the number of expansions
= k (k > 0), the expansion
function specified by series
is called (see for example
cosine.expansion
). Assuming is
the
expansion term for the
distance and that
are (estimated) coefficients for the expansion terms, the likelihood contribution for the
distance is,
Value
A numeric vector the same length and order as dist
containing the likelihood contribution for corresponding distances in dist
.
Assuming L
is the returned vector from one of these functions, the full log likelihood of all the data is -sum(log(L), na.rm=T)
. Note that the
returned likelihood value for distances less than w.lo
or greater than w.hi
is NA
, and thus it is prudent to use na.rm=TRUE
in the
sum. If scale
= TRUE, the integral of the likelihood from w.lo
to w.hi
is 1.0. If scale
= FALSE, the integral of the likelihood is
arbitrary.
See Also
dfuncEstim
,
halfnorm.like
,
uniform.like
,
hazrate.like
,
Gamma.like
Examples
## Not run:
set.seed(238642)
x <- seq(0, 100, length=100)
# Plots showing effects of changes in parameter Beta
plot(x, negexp.like(0.01, x), type="l", col="red")
plot(x, negexp.like(0.05, x), type="l", col="blue")
# Estimate 'negexp' distance function
Beta <- 0.01
x <- rexp(1000, rate=Beta)
dfunc <- dfuncEstim(x~1, likelihood="negexp")
plot(dfunc)
## End(Not run)