uniform.like  R Documentation 
Compute uniformlike 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 It has been replaced by the more appropriately named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
.
uniform.like(
a,
dist,
covars = NULL,
w.lo = 0,
w.hi = max(dist),
series = "cosine",
expansions = 0,
scale = TRUE,
pointSurvey = FALSE
)
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 
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.
If 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, 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)).
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