| print.secr {secr} | R Documentation |
Print results from fitting a spatially explicit capture–recapture model.
## S3 method for class 'secr': print (x, newdata = NULL, alpha = 0.05, deriv = FALSE, ...)
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) |
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).
Murray Efford murray.efford@otago.ac.nz
Burnham, K. P. and Anderson, D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Second edition. New York: Springer-Verlag.
## load & print previously fitted null (constant parameter) model data(secrdemo) print(secrdemo.0) print(secrdemo.CL, deriv = TRUE)