uniform.like {Rdistance} | R Documentation |
uniform.like - Uniform distance likelihood
Description
Compute uniform-like distribution for
distance functions. This function was present in Rdistance
version < 2.2.0. It has been replaced by the more appropriately named
logistic.like
.
Usage
uniform.like(
a,
dist,
covars = NULL,
w.lo = 0,
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 whether covariates and
|
dist |
A numeric vector containing observed distances with measurement units. |
covars |
Data frame containing values of covariates at
each observation in |
w.lo |
Scalar value of the lowest observable distance, with measurement
units.
This is the left truncation sighting distance. Values less than
|
w.hi |
Scalar value of the largest observable distance, with measurement
units.
This is the right truncation sighting distance.
Values greater than |
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 into a density function, i.e., so that it integrates
to 1. This parameter is used
to stop recursion in other functions.
If |
pointSurvey |
Boolean. TRUE if |
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,
the log likelihood of all 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
essential 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.
Expansion Terms
If 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, the likelihood contribution
for the
distance is,