predict.secr {secr} | R Documentation

## 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, 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)

### Arguments

`object` | `secr` object output from `secr.fit`, or list of `secr` objects (secrlist)
`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 `secr.make.newdata`
`byclass` | logical; if TRUE values are returned for each latent class in a mixture model, or class in a hybrid mixture (hcov) model

### Details

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 `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 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`.

### 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

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).

### Note

`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.

### References

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.

### 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))

    ## 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)

[Package secr version 3.2.1 Index]