| predict.secr {secr} | R Documentation |
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.
## 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, bytrap = 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 |
bytrap |
logical; if TRUE values are returned for each detector |
The variables in the various linear predictors are described in
secr-models.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 makeNewData; 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
esaPlot. detectpar calls predict.secr. Parameters
will be evaluated by default at base levels of the covariates, although
this may be overcome by passing a one-line 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(1--alpha)% confidence limit |
| ucl | upper 100(1--alpha)% 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 multi-session 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 y-coordinates, 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.
Overdispersion results in confidence intervals that are too narrow. See adjustVarD for a partial solution.
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.
secr.fit,
predictDsurface,
adjustVarD,
makeNewData
## 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)