Description Usage Arguments Details Value Note References See Also Examples
Evaluate a spatially explicit capture–recapture model. That is, compute the ‘real’ parameters corresponding to the ‘beta’ parameters of a fitted model for arbitrary levels of any variables in the linear predictor.
1 2 3 4 5 6 7 8 9 10  ## S3 method for class 'secr'
predict(object, newdata = NULL, realnames = NULL, type = c("response", "link"),
se.fit = TRUE, alpha = 0.05, savenew = FALSE, ...)
## S3 method for class 'secrlist'
predict(object, newdata = NULL, realnames = NULL, type = c("response", "link"),
se.fit = TRUE, alpha = 0.05, savenew = FALSE, ...)
## S3 method for class 'secr'
detectpar(object, ..., byclass = FALSE)

object 

newdata 
optional dataframe of values at which to evaluate model 
realnames 
character vector of real parameter names 
type 
character; type of prediction required. The default ("response") provides estimates of the ‘real’ parameters. 
se.fit 
logical for whether output should include SE and confidence intervals 
alpha 
alpha level for confidence intervals 
savenew 
logical for whether newdata should be saved 
... 
other arguments passed to 
byclass 
logical; if TRUE values are returned for each latent class in a mixture model, or class in a hybrid mixture (hcov) model 
The variables in the various linear predictors are described in
secrmodels.pdf and listed for the particular model in the
vars
component of object
.
Optional newdata
should be a dataframe with a column for each of
the variables in the model (see ‘vars’ component of object
). If
newdata
is missing then a dataframe is constructed automatically.
Default newdata
are for a naive animal on the first occasion;
numeric covariates are set to zero and factor covariates to their base
(first) level. From secr 3.1.4 the argument ‘all.levels’ may be passed
to secr.make.newdata
; if TRUE then the default newdata includes
all factor levels.
realnames
may be used to select a subset of parameters.
Standard errors for parameters on the response (real) scale are by the delta method (Lebreton et al. 1992), and confidence intervals are backtransformed from the link scale.
The value of newdata
is optionally saved as an attribute.
detectpar
is used to extract the detection parameter estimates
from a simple model to pass to functions such as
esa.plot
. detectpar
calls predict.secr
. Parameters
will be evaluated by default at base levels of the covariates, although
this may be overcome by passing a oneline newdata
to
predict
via the ... argument. Groups and mixtures are a
headache for detectpar
: it merely returns the estimated detection
parameters of the first group or mixture.
If the ‘a0’ parameterization has been used in secr.fit
(i.e.,
object$details$param == 3
) then detectpar
automatically
backtransforms (a0, sigma) to (g0, sigma) or (lambda0, sigma) depending
on the value of object$detectfn
.
When se.fit
= FALSE, a dataframe identical to newdata
except for the addition of one column for each ‘real’ parameter. Otherwise, a list with one component for each row in newdata
. Each component is a dataframe with one row for each ‘real’ parameter (density, g0, sigma, b) and columns as below
link  link function 
estimate  estimate of real parameter 
SE.estimate  standard error of the estimate 
lcl  lower 100(1alpha)% confidence limit 
ucl  upper 100(1alpha)% confidence limit 
When newdata
has only one row, the structure of the list is
‘dissolved’ and the return value is one data frame.
For detectpar
, a list with the estimated values of detection
parameters (e.g., g0 and sigma if detectfn = "halfnormal"). In the case
of multisession data the result is a list of lists (one list per
session).
predictDsurface
should be used for predicting density at many
points from a model with spatial variation. This deals automatically
with scaling of x and ycoordinates, and is much is faster than
predict.secr. The resulting Dsurface object has its own plot method.
The argument ‘scaled’ was removed from both predict methods in version 2.10 as the scaleg0 and scalesigma features had been superceded by other parameterisations.
Lebreton, J.D., Burnham, K. P., Clobert, J. and Anderson, D. R. (1992) Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs 62, 67–118.
1 2 3 4 5 6 7 8 9 10 11 12 13  ## load previously fitted secr model with trap response
## and extract estimates of `real' parameters for both
## naive (b = 0) and previously captured (b = 1) animals
predict (secrdemo.b, newdata = data.frame(b = 0:1))
## OR from secr 3.1.4
predict (secrdemo.b, all.levels = TRUE)
temp < predict (secrdemo.b, all.levels = TRUE, save = TRUE)
attr(temp, "newdata")
detectpar(secrdemo.0)

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