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 secr
predict(object, newdata = NULL, realnames = NULL, type = c("response", "link"),
se.fit = TRUE, alpha = 0.05, savenew = FALSE, ...)# S3 method for secrlist
predict(object, newdata = NULL, realnames = NULL, type = c("response", "link"),
se.fit = TRUE, alpha = 0.05, savenew = FALSE, ...)
# S3 method for secr
detectpar(object, ..., byclass = FALSE)
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).
secr object output from secr.fit, or list
of secr objects (secrlist)
optional dataframe of values at which to evaluate model
character vector of real parameter names
character; type of prediction required. The default ("response") provides estimates of the `real' parameters.
logical for whether output should include SE and confidence intervals
alpha level for confidence intervals
logical for whether newdata should be saved
other arguments passed to makeNewData
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
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
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 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.
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)
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