make.ddf.analysis.list: Creates a list of DDF.Analysis objects
In DSsim: Distance Sampling Simulations

Description Usage Arguments Details Value Author(s) See Also Examples

This method creates a list of DDF.Analysis objects each of which describes a model to fit to the distance data. The simulation will fit each of these models to the data generated in the simulation and select the model with the minimum criteria value.

make.ddf.analysis.list(
  dsmodel = list(~cds(key = "hn", formula = ~1)),
  mrmodel = NULL,
  method = "ds",
  criteria = "AIC",
  analysis.strata = data.frame(),
  truncation = 50,
  binned.data = FALSE,
  cutpoints = numeric(0)
)

`dsmodel`	list of distance sampling model formula specifying the detection function (see `?ddf` for further details)
`mrmodel`	not yet implemented
`method`	character only "ds" normal distance sampling currently implemented
`criteria`	character model selection criteria (AIC, AICc, BIC) - only AIC implemented at present.
`analysis.strata`	Dataframe with two columns ("design.id" and "analysis.id"). The former gives the strata names as defined in the design (i.e. the region object) the second specifies how they should be grouped (into less strata) for the analyses
`truncation`	numeric truncation distance for analyses
`binned.data`	logical whether the data should be analsed in bins
`cutpoints`	gives the cutpoints of the binned data

By default this function creates a half-normal detection function model dsmodel = list(~cds(key = "hn", formula = ~1)) with a truncation distance of 75.

list of objects of class DDF.Analysis

Laura Marshall

ddf in library(mrds)

# A simple half-normal "ds" model can be created using the default values
ddf.analyses <- make.ddf.analysis.list()

# To incorporate model selection between a 'hn' and 'hr' model:
ddf.analyses <- make.ddf.analysis.list(dsmodel = list(~cds(key = "hn",
 formula = ~1),~cds(key = "hr", formula = ~1)), method = "ds", 
 criteria = "AIC")