mlEstimates

Estimate parameters of a distance function using maximum likelihood.

Distance-sampling (<doi:10.1007/978-3-319-19219-2>)
estimates density and abundance of survey targets (e.g., animals) when
detection probability declines with distance.
Distance-sampling is popular in ecology,
especially when survey targets are observed from aerial platforms (e.g.,
airplane or drone), surface vessels (e.g., boat or truck), or along
walking transects.
Distance-sampling includes line-transect studies that measure observation
distances as the closest approach of the sample route (transect) to the target
(i.e., perpendicular off-transect distance), and point-transect studies that
measure observation distances from stationary observers to
the target (i.e., radial distance).
The routines included here fit smooth (parametric) curves to
histograms of observation distances
and use those functions to compute effective sampling distances, density of
targets in the surveyed area, and abundance
of targets in a surrounding study area. Curve shapes include the
half-normal, hazard rate, and negative exponential functions.
Physical measurement units are required and used throughout to
ensure density is reported correctly. The help files
are extensive and have been vetted by multiple authors.

Trent McDonald

Rdistance

Density and Abundance from Distance-Sampling Surveys

Jason Carlisle

Aidan McDonald

Ryan Nielson

Ben Augustine

James Griswald

Patrick McKann

Lacey Jeroue

Hoffman Abigail

Kleinsausser Michael

Joel Reynolds

Pham Quang

Earl Becker

Aaron Christ

Brook Russelland

Stefan Emmons

Will McDonald

Reid Olson

mlEstimates function

<dl><dt>ml</dt>
<dd>Either a Rdistance 'model frame' or an Rdistance 
'fitted object'. Both are of class "dfunc". 
Rdistance 'model frames' are lists containing components 
necessary to estimate a distance function, but no estimates. 
Rdistance 'model frames' are typically
produced by calls to <code>parseModel</code>. 
Rdistance 'fitted objects'
are typically produced by calls to <code>dfuncEstim</code>.
'Fitted objects' are 'model frames'
with additional components such as the parameters estimates, 
log likelihood value, convergence information, and the variance-
covariance matrix of the parameters.</dd>
<dt>strt.lims</dt>
<dd>A list containing start, low, and high limits for 
parameters of the requested likelihood. This list is typically produced
by a call to <code>startLimits</code>.</dd></dl>

Arguments

mlEstimates - Distance function maximum likelihood estimates — mlEstimates

<dl>

<dt>ml</dt>
<dd>Either a Rdistance 'model frame' or an Rdistance 
'fitted object'. Both are of class "dfunc". 
Rdistance 'model frames' are lists containing components 
necessary to estimate a distance function, but no estimates. 
Rdistance 'model frames' are typically
produced by calls to <code>parseModel</code>. 
Rdistance 'fitted objects'
are typically produced by calls to <code>dfuncEstim</code>.
'Fitted objects' are 'model frames'
with additional components such as the parameters estimates, 
log likelihood value, convergence information, and the variance-
covariance matrix of the parameters.</dd>


<dt>strt.lims</dt>
<dd>A list containing start, low, and high limits for 
parameters of the requested likelihood. This list is typically produced
by a call to <code>startLimits</code>.</dd>

</dl>

mlEstimates - Distance function maximum likelihood estimates

mlEstimates: mlEstimates - Distance function maximum likelihood estimates

Description

Usage

Value

Arguments