print.ipsecr: Print, Trim or Summarise ipsecr Object

Description

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

Usage

# S3 method for ipsecr
print (x, newdata = NULL, alpha = 0.05, call = TRUE, ...)
# S3 method for ipsecr
summary (object, newdata = NULL, alpha = 0.05, ...)
# S3 method for ipsecr
trim (object, drop = c('call', 'proxyfn', 'mask', 'sim.lm'), keep = NULL)

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	ipsecr version, and date and time fitting started
traps	detector summary
capthist	capthist summary
mask	mask summary
modeldetails	miscellaneous model characteristics
coef	table of fitted coefficients with CI
predicted	predicted values (`real' parameter estimates)

Arguments

x: ipsecr object output from ipsecr.fit
object: ipsecr object output from ipsecr.fit
newdata: optional dataframe of values at which to evaluate model
alpha: alpha level
call: logical; if TRUE the call is printed
...: other arguments (not used)
drop: character vector identifying components to be dropped
keep: character vector identifying components to be kept

Details

Results from print.ipsecr 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	ipsecr 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$newdetector specified
N animals	number of distinct animals detected
N detections	number of detections
N occasions	number of sampling occasions
Mask area
Model	model formula for each `real' parameter
Fixed (real)	fixed real parameters
Detection fn	detection function type (halfnormal or hazard-rate)
Distribution	spatial model (details$distribution)
N parameters	number of parameters estimated
Design points	number of vertices and centre points
Simulations per box	total number
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

Examples

Run this code


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

print(ipsecrdemo)

summary(ipsecrdemo)