negexp.like  R Documentation 
Computes the negative exponential form of a distance function
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
)
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 
The negative exponential likelihood is
f(xa) = \exp(ax)
where a
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 h_{ij}(x)
is
the j^{th}
expansion term for the i^{th}
distance and that
c_1, c_2, \dots, c_k
are (estimated) coefficients for the expansion terms, the likelihood contribution for the i^{th}
distance is,
f(xa,b,c_1,c_2,\dots,c_k) = f(xa,b)(1 + \sum_{j=1}^{k} c_j h_{ij}(x)).
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.
dfuncEstim
,
halfnorm.like
,
uniform.like
,
hazrate.like
,
Gamma.like
## 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)
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