secr (version 2.5.0)

predict.secr: SECR Model Predictions

Description

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.

Usage

## S3 method for class 'secr':
predict(object, newdata = NULL, se.fit = TRUE, alpha = 0.05, 
    savenew = FALSE, scaled = FALSE, \dots)

detectpar (object, ...)

Arguments

object

secr object output from secr.fit

newdata

optional dataframe of values at which to evaluate model

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

scaled

logical for scaling of sigma and g0 (see Details)

...

other arguments

Value

• 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 ll{ 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).

Details

The variables in the various linear predictors are described in secr models 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. Standard errors are by the delta method (Lebreton et al. 1992). Confidence intervals are backtransformed from the link scale. The argument scaled applies only to the detection parameters g0 and sigma, and only to models fitted with scalesigma or scaleg0 switched on. If scaled is TRUE then each estimate is multiplied by its scale factor (1/D^0.5 and 1/sigma^2 respectively). 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.

References

Lebreton, J.-D., Burnham, K. P., Clobert, J., Anderson, D. R. (1992) Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs 62, 67--118.

See Also

secr.fit, predictDsurface

Examples

## 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))

temp <- predict (secrdemo.b, newdata = data.frame(b=0:1), 
    save = TRUE)
attr(temp, "newdata")

detectpar(secrdemo.0)