F.automated.CDA: F.automated.CDA - Automated classical distance analysis.

Description

Perform automated classical distance function selection and estimation of abundance.

Usage

F.automated.CDA(dist, group.size = 1, area = 1, total.trans.len = 1, w.lo = 0, w.hi = max(dist), 
    likelihoods = c("halfnorm", "hazrate", "uniform", "negexp", "Gamma"), 
    series = c("cosine", "hermite", "simple"), expansions = 0:3, plot = TRUE, ...)

Arguments

dist

The vector of observed off-transect distances. All values in dist must be between w.lo and w.hi.

group.size

Vector of group sizes for every element in dist. This vector is replicated as necessary to be the same length as dist. Default value is 1, which assumes all sightings were individuals.

area

Size of the study area, in squared units of distance measures (e.g., if units of off-transect distances is meters, study area size should be in square meters). Default value of 1 produces density estimates. See documentation for F.abun

total.trans.len

Total length of all transects in the study area, in units of the distance measures (e.g., if units of off-transect distances is meters, total transect length should be meters). Default value is 1.

w.lo

Lower or left-truncation limit of the distances in dist. This is the minimum possible off-transect distance. Default is 0.

w.hi

Upper or right-truncation limit of the distances in dist. This is the maximum off-transect distance that could be observed. Default is the maximum observed distance.

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 t

...

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 amoung 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) X 2 (series) X 4 (expansions) = 24 models. By default, every available (classic) distance function is fitted. The default specification results in 5 (likelihoods) X 3 (series) X 4 (expansions) = 60 fitted models. The model with lowest AIC is choosen as 'best', and estimation of abundance proceeds using that model.

Examples

Run this code

set.seed(234252)
x <- rnorm(1000) * 100
x <- x[ 0 < x & x < 100 ]

#   Assume surveyed 10000 m in study area of size 1000 hectares.
N <- F.automated.CDA( x, area=1000*10000, total.trans.len=1000, w.hi = 100, 
    likelihood=c("halfnorm", "uniform", "Gamma"), series=c("cosine", "simple"), 
    expansions=c(0,1,2))