summary.dfunc {Rdistance} | R Documentation |
Summarize a distance function object
Description
A summary method for distance functions.
Distance functions are produced by
dfuncEstim
(class dfunc
).
Usage
## S3 method for class 'dfunc'
summary(object, criterion = "AICc", ...)
Arguments
object |
An Rdistance model frame or fitted distance function,
normally produced by a call to |
criterion |
A string specifying the model fit criterion to print.
Must be one of "AICc" (the default),
"AIC", or "BIC". See |
... |
Included for compatibility with other print methods. Ignored here. |
Details
This function prints the following quantities:
-
‘Call’ : The original function call.
-
‘Coefficients’ : A matrix of estimated coefficients, their standard errors, and Wald Z tests.
-
‘Strip’ : The left (
w.lo
) and right (w.hi
) truncation values. -
‘Effective strip width or detection radius’ : ESW or EDR as computed by
effectiveDistance
. -
‘Probability of Detection’ : Probability of detecting a single target in the strip.
-
‘Scaling’ : The horizontal and vertical coordinates used to scale the distance function. Usually, the horizontal coordinate is 0 and the vertical coordinate is 1 (i.e., g(0) = 1).
-
‘Log likelihood’ : Value of the maximized log likelihood.
-
‘Criterion’ : Value of the specified fit criterion (AIC, AICc, or BIC).
The number of digits used in the printout is
controlled by options()$digits
.
Value
The input distance function object (object
), invisibly,
with the following additional components:
-
convMessage
: The convergence message. If the distance function is smoothed, the convergence message is NULL. -
effDistance
: The ESW or EDR. -
pDetect
: Probability of detection in the strip. -
AIC
: AICc, AIC, or BIC of the fit, whichever was requested. -
coefficients
: If the distance function has coefficients, this is the coefficient matrix with standard errors, Wald Z values, and p values. If the distance function is smoothed, it has no coefficients and this component is NULL.
See Also
dfuncEstim
, plot.dfunc
,
print.abund
, print.abund
Examples
# Load example sparrow data (line transect survey type)
data(sparrowDf)
# Fit half-normal detection function
dfunc <- sparrowDf |> dfuncEstim(formula=dist~1)
# Print results
summary(dfunc)
summary(dfunc, criterion="BIC")