Description Usage Arguments Details Value Author(s) See Also Examples

Computes the likelihood of sighting distances given a kernel smooth of the histogram.

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`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 |

`w.hi` |
Scalar value of the largest observable distance.
This is the |

`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.

Trent McDonald, WEST, Inc. tmcdonald@west-inc.com

`dfuncSmu`

,
`hazrate.like`

,
`uniform.like`

,
`negexp.like`

,
`halfnorm.like`

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