smu.like | R Documentation |
Computes the likelihood of sighting distances given a kernel smooth of the histogram.
smu.like(
a,
dist,
covars = NULL,
w.lo = 0,
w.hi,
scale = TRUE,
series = NULL,
expansions = 0,
pointSurvey = FALSE
)
a |
A data frame containing the smooth. This data frame
must contain at least an |
dist |
A numeric vector containing the observed distances. |
covars |
Not used in smoothed distance functions.
Included for compatibility with other distance likelihoods
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 |
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 |
series |
Not used in smoothed distance functions.
Included for compatibility with other distance likelihoods
in |
expansions |
Not used in smoothed distance functions.
Included for compatibility with other distance likelihoods
in |
pointSurvey |
Boolean. TRUE if distances in |
The approx
function is used to evaluate
the smooth function at all sighting distances.
Distances outside the range w.lo
to w.hi
are
set to NA
and hence not included.
A numeric vector the same length and order
as dist
containing the
likelihood contribution (height of the smoothed function) for
all distances in dist
.
Assuming L
is the vector returned by this function,
the negative log likelihood of the sighting distances
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
,
hence na.rm=TRUE
in the sum.
If scale
= TRUE, the area under the smoothed curve
between w.lo
and w.hi
is 1.0. If scale
= FALSE,
the integral of the smoothed curve is something else.
dfuncSmu
,
hazrate.like
,
uniform.like
,
negexp.like
,
halfnorm.like
set.seed(238642)
d <- units::set_units(abs(rnorm(100)), "in")
dfunc <- dfuncSmu(d~1)
L <- smu.like(a=dfunc$parameters,
dist=dfunc$detections$dist,
w.lo=dfunc$w.lo,
w.hi=dfunc$w.hi,
scale=TRUE)
-sum(log(L), na.rm=TRUE) # the negative log likelihood
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