Rdistance (version 1.2.2)

F.automated.CDA: Automated classical distance analysis.

Description

Perform automated classical detection function selection and estimation of abundance.

Usage

F.automated.CDA(detection.data, transect.data, w.lo=0, w.hi=max(dist),
likelihoods=c("halfnorm", "hazrate", "uniform", "negexp", "Gamma"),
series=c("cosine", "hermite", "simple"), expansions=0:3, warn=TRUE,
area=1, ci=0.95, R=500, bs.method="transects", plot.bs=FALSE, plot=TRUE, ...)

Arguments

detection.data

This parameter is passed to F.dfunc.estim and F.abund.estim. See F.abund.estim documentation for definition.

transect.data

This parameter is passed to F.abund.estim. See F.abund.estim documentation for definition.

w.lo

This parameter is passed to F.dfunc.estim. See F.dfunc.estim documentation for definition.

w.hi

This parameter is passed to F.dfunc.estim. See F.dfunc.estim documentation for definition.

warn

This parameter is passed to F.dfunc.estim. See F.dfunc.estim documentation for definition.

area

This parameter is passed to F.abund.estim. See F.abund.estim documentation for definition.

ci

This parameter is passed to F.abund.estim. See F.abund.estim documentation for definition.

R

This parameter is passed to F.abund.estim. See F.abund.estim documentation for definition.

bs.method

This parameter is passed to F.abund.estim. See F.abund.estim documentation for definition.

plot.bs

This parameter is passed to F.abund.estim. See F.abund.estim documentation for definition.

likelihoods

Vector of strings specifying the likelihoods to consider during model selection. Valid values at present are "uniform", "halfnorm", "hazrate", "negexp", and "Gamma". See Details for the models this routine considers.

series

Vector of series types to consider during model selection. Valid values are 'simple', 'hermite', and 'cosine'. See Details for the models this routine considers.

expansions

Vector of the number of expansion terms to consider during model selection. Valid values are 0 through 3. See Details for the models this routine considers.

plot

Logical scalar specifying whether to plot models during model selection. If TRUE, a histogram with fitted distance function is plotted for every fitted model. The function pauses between each plot and prompts the user for whether they want

...

Additional parameters passed to F.dfunc.estim, which in turn are passed to F.gx.estim. These include x.scl, g.x.scl, and observer for estimating double observer probabilities.

Value

• An 'abundance estimate' object (see F.abund.estim and F.dfunc.estim). Returned abundance estimates are based on the best fitting distance function among those fitted.

Details

During model selection, each series and number of expansions is crossed with each of the likelihoods. For example, if likelihoods has 3 elements, series has 2 elements, and expansions has 4 elements, the total number of models fitted is 3 (likelihoods) * 2 (series) * 4 (expansions) = 24 models. By default, every available (classic) distance function is fitted. The default specification results in 5 (likelihoods) * 3 (series) X * (expansions) = 60 fitted models. The model with lowest AIC is choosen as 'best', and estimation of abundance proceeds using that model.

See Also

F.dfunc.estim, F.abund.estim

Examples

# Load the example datasets for sparrow detections and transects from package
data(sparrow.detections)
data(sparrow.transects)

# Compute perpendicular, off-transect distances from the observer's sight distance and angle
sparrow.detections$dist <- perp.dists(obs.dist=sparrow.detections$sightdist,
                                  obs.angle=sparrow.detections$sightangle)

# Automate fitting multiple detection functions
# And estimate abundance (density per ha in this case) given the 'best' detection function
# Note, area=10000 converts to density per hectare (for distances measured in meters)
# Note, a person should do more than R=20 iterations 
F.automated.CDA(detection.data=sparrow.detections, transect.data=sparrow.transects,
                likelihood=c("halfnorm", "hazrate", "negexp"),
                series=c("cosine", "simple"),
                expansions=c(0, 1), area=10000, R=20, ci=0.95, plot.bs=FALSE,
                bs.method="transects", w.hi=150, plot=TRUE)