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:

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)

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.

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.

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
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)

}