predict.ds {mrds} | R Documentation |
Predictions from mrds
models
Description
Predict detection probabilities (or effective strip widths/effective areas of detection) from a fitted distance sampling model using either the original data (i.e. "fitted" values) or using new data.
Usage
## S3 method for class 'ds'
predict(object, newdata=NULL, compute=FALSE,
int.range=NULL, esw=FALSE, se.fit=FALSE, ...)
## S3 method for class 'io.fi'
predict(object, newdata=NULL, compute=FALSE,
int.range=NULL, integrate=FALSE, ...)
## S3 method for class 'io'
predict(object, newdata=NULL, compute=FALSE,
int.range=NULL, ...)
## S3 method for class 'trial'
predict(object, newdata=NULL, compute=FALSE,
int.range=NULL, ...)
## S3 method for class 'trial.fi'
predict(object, newdata=NULL, compute=FALSE,
int.range=NULL, integrate=FALSE, ...)
## S3 method for class 'rem'
predict(object, newdata=NULL, compute=FALSE,
int.range=NULL, ...)
## S3 method for class 'rem.fi'
predict(object, newdata=NULL, compute=FALSE,
int.range=NULL, integrate=FALSE, ...)
Arguments
object |
|
newdata |
new |
compute |
if |
int.range |
integration range for variable range analysis; either vector or 2 column matrix. |
esw |
if |
se.fit |
for |
... |
for S3 consistency |
integrate |
for |
Details
The first 4 arguments are the same in each predict function. The latter 2
are specific to certain functions. For line transects, the effective strip
half-width (esw=TRUE
) is the integral of the fitted detection
function over either 0 to W or the specified int.range
. The
predicted detection probability is the average probability which is simply
the integral divided by the distance range. For point transect models,
esw=TRUE
calculates the effective area of detection (commonly
referred to as "nu", this is the integral of 2/width^2 * rg(r)
.
Fitted detection probabilities are stored in the model
object and
these are returned unless compute=TRUE
or newdata
is
specified. compute=TRUE
is used to estimate numerical derivatives for
use in delta method approximations to the variance.
For method="io.fi"
or method="trial.fi"
if
integrate=FALSE
, predict
returns the value of the conditional
detection probability and if integrate=TRUE
, it returns the average
conditional detection probability by integrating over x (distance) with
respect to a uniform distribution.
Note that the ordering of the returned results when no new data is supplied
(the "fitted" values) will not necessarily be the same as the data supplied
to ddf
, the data (and hence results from predict
) will
be sorted by object ID (object
) then observer ID (observer
).
Value
For all but the exceptions below, the value is a list with a single
element: fitted
, a vector of average detection probabilities or esw
values for each observation in the original data ornewdata
For predict.ds
, if se.fit=TRUE
there is an additional element
$se.fit
, which contains the standard errors of the probabilities of
detection or ESW.
For predict.io.fi
,predict.trial.fi
,predict.rem.fi
with
integrate=TRUE
, the value is a list with one element: fitted
,
which is a vector of integrated (average) detection probabilities for each
observation in the original data or newdata
.
For predict.io.fi
, predict.trial.fi
, or predict.rem.fi
with integrate=FALSE
, the value is a list with the following
elements:
fitted
p(y)
valuesp1
p_{1|2}(y)
, conditional detection probability for observer 1p2
p_{2|1}(y)
, conditional detection probability for observer 2fitted
p_.(y) = p_{1|2}(y) + p_{2|1}(y) - p_{1|2}(y) * p_{2|1}(y)
, conditional detection probability of being seen by either observer
Note
Each function is called by the generic function predict
for the
appropriate ddf
model object. They can be called directly by the
user, but it is typically safest to use predict
which calls the
appropriate function based on the type of model.
Author(s)
Jeff Laake, David L Miller