print.secr: 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,
call = TRUE, ...)

Arguments

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

call

logical; if TRUE the call is printed

...

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. ll{ call the function call (optional) version,time secr version, and date and time fitting started Detector type `single', `multi', `proximity' etc. Detector number number of detectors Average spacing x-range y-range N animals number of distinct animals detected N detections number of detections N occasions number of sampling occasions Mask area Model model formula for each `real' parameter Fixed (real) 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 (optional) } Derived parameters (see derived) are computed only if deriv = TRUE.

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.

Examples

Run this code

## load & print previously fitted null (constant parameter) model
print(secrdemo.0)

print(secrdemo.CL, deriv = TRUE)