filter.p: Filter for Presence and Persistence
In paleoMAS: Paleoecological Analysis

Description Usage Arguments Details Value Author(s) References Examples

This function reduces a given dataset based on filters for minimum presence (abundance) AND minimum persistence (number of samples), or maximum persitence.

1	filter.p(x, rare=TRUE, presen = 1, persist = 0.05)

`x`	Matrix or dataframe with taxa in columns and samples in rows.
`rare`	Argument indicating if filter aims rare or common taxa. If `rare=TRUE`, the filter eliminates rare taxa with presence and persitence indicated in the respective arguments. If `rare=FALSE`, the filter eliminates taxa with a persistence higher than that indicated in teh argument `persist`.
`presen`	Criterion for minimum percentual presence, 1 percent by default. Only relevant if `rare=TRUE`.
`persist`	Criterion for minimum persistence as a fraction of the number of samples where the taxon is expected to occur, 0.05 of the total number of samples by default.

This function applies both the presence and persistence filters when rare=TRUE. If the user desires to apply only one of the filters at a given time, a criterion that is met by all elements (taxa) should be chosen, e.g. persist=0. If rare=FALSE, only the criterion of being under the given persitence threshold is applied.

Returns a list with three components

`filtered`	Reduced dataset after both filters are applied.
`filter`	This component is returned only when `rare=TRUE`. Matrix with three columns: `n`, number of samples where taxon is present; `n over minimum presence`, number of samples where percentage is greater than the defined minimum or filter; `quality`, binary that lets the user know whether or not a taxon meets the filter criteria.
`result`	List with two or three components: `percentage`, minimum presence, only relevant when `rare=TRUE`; `minimum` or `maximum`, minimum or maximum persistence; and `number of taxa`, total number of taxa that meet the filter criteria.

Alexander Correa-Metrio, Kenneth R. Cabrera, Dunia H. Urrego.

Correa-Metrio, A., K.R. Cabrera, and M.B. Bush. 2010. Quantifying ecological change through discriminant analysis: a paleoecological example from the Peruvian Amazon. Journal of Vegetation Science 21: 695-704.

#For a minimum presence of 2 percent in 20 percent of the samples
data(quexilper)
filter.p(quexilper)
quexilfil<-filter.p(quexilper,presen=2,persist=0.2)
#Filtered database
quexilfil$filtered