Add biodiversity point dataset to a distribution object (presence-only)Source:
This function adds a presence-only biodiversity dataset to a distribution object.
add_biodiversity_poipo( x, poipo, name = NULL, field_occurrence = "observed", formula = NULL, family = "poisson", link = NULL, weight = 1, separate_intercept = TRUE, docheck = TRUE, pseudoabsence_settings = NULL, ... ) # S4 method for BiodiversityDistribution,sf,ANY,character,ANY,character,ANY,numeric,logical,logical,ANY add_biodiversity_poipo(x,poipo,name,field_occurrence,formula,family,link,weight,separate_intercept,docheck,pseudoabsence_settings,...)
sfobject of presence-only point occurrences.
The name of the biodiversity dataset used as internal identifier.
characterstating the family to be used (Default:
characterto overwrite the default link function (Default:
numericvalue acting as a multiplier with regards to any weights used in the modelling. Larger weights indicate higher weighting relative to any other datasets. By default set to
1if only one dataset is added. A
vectoris also supported but must be of the same length as
"poipo". Note: Weights are reformated to the inverse for models with area offsets (e.g. 5 is converted to 1/5).
logicalon whether additional checks should be performed (e.g. intersection tests) (Default:
pseudoabs_settings()created settings object.
Other parameters passed down to the object. Normally not used unless described in details.
Adds biodiversity data to distribution object.
This function allows to add presence-only biodiversity records to a distribution ibis.iSDM Presence-only data are usually modelled through an inferential model (see Guisan and Zimmerman, 2000) that relate their occurrence in relation to environmental covariates to a selected sample of 'background' points. The most common approach for estimation and the one supported by this type of dataset are poisson-process models (PPM) in which presence-only points are fitted through a down-weighted Poisson regression. See Renner et al. 2015 for an overview.
Guisan A. and Zimmerman N. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135: 147–186.
Renner, I. W., J. Elith, A. Baddeley, W. Fithian, T. Hastie, S. J. Phillips, G. Popovic, and D. I. Warton. 2015. Point process models for presence-only analysis. Methods in Ecology and Evolution 6:366–379.