print.secr {secr} | R Documentation

## Print secr Object

### Description

Print results from fitting a spatially explicit capture–recapture model.

### Usage

    ## S3 method for class 'secr':
    print (x, newdata = NULL, alpha = 0.05, deriv = FALSE, ...)

### Arguments

`x` | `secr` object output from `secr.fit`
`newdata` | optional dataframe of values at which to evaluate model
`alpha` | alpha level
`deriv` | logical for calculation of derived D and esa
`...` | other arguments (not used currently)

### Details

Results are potentially complex and depend upon the analysis (see below). Optional `newdata` should be a dataframe with a column for each of the variables in the model. 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. Confidence intervals are 100 (1 – alpha) % intervals.

call | the function call
time | date and time of completion
N animals | number of distinct animals detected
N captures | number of detections
N occasions | number of sampling occasions
N detectors | number of detectors
Detector type | 'single', 'multi', 'proximity' etc.
Model | model formula for each 'real' parameter
Fixed | fixed real parameters
Detection fn | detection function type (halfnormal or hazard-rate)
N parameters | number of parameters estimated
Log likelihood | log likelihood
AIC | Akaike's information criterion
AICc | AIC with small sample adjustment (Burnham and Anderson 2002)
Beta parameters | coef of the fitted model, SE and confidence intervals
vcov | variance-covariance matrix of beta parameters
Real parameters | fitted (real) parameters evaluated at base levels of covariates
Derived parameters | derived estimates of density and mean effective sampling area

Derived parameters (see `derived`) are computed only for models fitted by maximizing the conditional likelihood (CL = TRUE).

### Author(s)

Murray Efford murray.efford@otago.ac.nz

### References

Burnham, K. P. and Anderson, D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Second edition. New York: Springer-Verlag.

### See Also

`AIC.secr`, `secr.fit`

### Examples

    ## load & print previously fitted null (constant parameter) model
    data(secrdemo)
    print(secrdemo.0)
    print(secrdemo.CL, deriv = TRUE)

[Package secr version 1.3.0 Index]