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secr (version 4.6.10)

print.secr: Print or Summarise secr Object

Description

Print results from fitting a spatially explicit capture--recapture model or generate a list of summary values.

Usage

# S3 method for secr
print (x, newdata = NULL, alpha = 0.05, deriv = FALSE, call = TRUE, ...)
# S3 method for secr
summary (object, newdata = NULL, alpha = 0.05, deriv = FALSE, ...)

Value

The summary method constructs a list of outputs similar to those printed by the print method, but somewhat more concise and re-usable:

versiontimesecr version, and date and time fitting started
trapsdetector summary
capthistcapthist summary
maskmask summary
modeldetailsmiscellaneous model characteristics (CL etc.)
AICtablesingle-line output of AIC.secr
coeftable of fitted coefficients with CI
predictedpredicted values (`real' parameter estimates)
derivedoutput of derived.secr (optional)

Arguments

x

secr object output from secr.fit

object

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 optionally passed to derived.secr

Details

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.

callthe function call (optional)
version,timesecr version, date and time fitting started, and elapsed time
Detector type`single', `multi', `proximity' etc.
Detector numbernumber of detectors
Average spacing
x-range
y-range
New detector typeas fitted when details$newdetector specified
N animalsnumber of distinct animals detected
N detectionsnumber of detections
N occasionsnumber of sampling occasions
Mask area
Modelmodel formula for each `real' parameter
Fixed (real)fixed real parameters
Detection fndetection function type (halfnormal or hazard-rate)
N parametersnumber of parameters estimated
Log likelihoodlog likelihood
AICAkaike's information criterion
AICcAIC with small sample adjustment (Burnham and Anderson 2002)
Beta parameterscoef of the fitted model, SE and confidence intervals
vcovvariance-covariance matrix of beta parameters
Real parametersfitted (real) parameters evaluated at base levels of covariates
Derived parametersderived 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.

See Also

AIC.secr, secr.fit

Examples

Run this code

## 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))

if (FALSE) {

print(secrdemo.CL, deriv = TRUE)

}

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