Print or Summarise secr Object
Print results from fitting a spatially explicit capture–recapture model or generate a list of summary values.
## S3 method for class 'secr' print(x, newdata = NULL, alpha = 0.05, deriv = FALSE, call = TRUE, ...) ## S3 method for class 'secr' summary(object, newdata = NULL, alpha = 0.05, deriv = FALSE, ...)
x |
|
object |
|
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 optionally passed to derived.secr |
Results from print.secr 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 (optional) |
| version,time | secr version, date and time fitting started, and elapsed time |
| Detector type | `single', `multi', `proximity' etc. |
| Detector number | number of detectors |
| Average spacing | |
| x-range | |
| y-range | |
| New detector type | as fitted when details$newdetector specified |
| 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.
The summary method constructs a list of outputs similar to those printed by the print method, but somewhat more concise and re-usable:
| versiontime | secr version, and date and time fitting started |
| traps | detector summary |
| capthist | capthist summary |
| mask | mask summary |
| modeldetails | miscellaneous model characteristics (CL etc.) |
| AICtable | single-line output of AIC.secr |
| coef | table of fitted coefficients with CI |
| predicted | predicted values (`real' parameter estimates) |
| derived | output of derived.secr (optional) |
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 print(secrdemo.0) summary(secrdemo.0) ## combine AIC tables from list of summaries do.call(AIC, lapply(list(secrdemo.b, secrdemo.0), summary)) ## Not run: print(secrdemo.CL, deriv = TRUE) ## End(Not run)
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