multTSA: Trend Surface Analysis for multiple species

View source: R/multTSA.R

multTSAR Documentation

Trend Surface Analysis for multiple species

Description

This function performs trend surface analysis for one or more species at a time. It converts categorical presence-absence (1-0) data into continuous surfaces denoting the spatial trend in species' occurrence patterns.

Usage

multTSA(data, sp.cols, coord.cols, id.col = NULL, degree = 3, 
step = TRUE, criterion = "AIC", type = "P", Favourability = FALSE, 
suffix = "_TS", save.models = FALSE, verbosity = 2, ...)

Arguments

data

a matrix or data frame containing, at least, two columns with spatial coordinates, and one column per species containing their presence (1) and absence (0) data, with localities in rows.

sp.cols

names or index numbers of the columns containing the species presences and absences in data. Must contain only zeros (0) for absences and ones (1) for presences.

coord.cols

names or index numbers of the columns containing the spatial coordinates in data (x and y, or longitude and latitude, in this order!).

id.col

optionally, the name or index number of a column (to be included in the output) containing locality identifiers in data.

degree

the degree of the spatial polynomial to use (see Details). The default is 3.

step

logical value indicating whether the regression of presence-absence on the spatial polynomial should do a stepwise inclusion of the polynomial terms (using the step function with default settings, namely backward AIC selection), rather than forcing all terms into the equation. The default is TRUE.

criterion

character value indicating whether the backward stepwise selection of variables (if step = TRUE) should be made according to "AIC" (the default, using the step function) or to "significance" (using the modelTrim function).

type

the type of trend surface to obtain. Can be either "Y" for the raw polynomial equation (i.e. in the scale of the predictors, e.g. if you want to use the spatial trend as a predictor variable in a model), "P" for the logit-transformed probability (e.g. if you want to use the output as a prediction of presence probability based on spatial trend alone), or "F" for spatial favourability, i.e., prevalence-independent probability (see Fav).

Favourability

deprecated argument; linktype should now be used instead, although (at least for the timebeing) this argument will still be accepted (with Favourability=TRUE internally resulting in type="F") for back-compatibility.

suffix

character indicating the suffix to add to the trend surface columns in the resulting data frame. The default is "_TS".

save.models

logical value indicating whether the models obtained from the regressions should be saved and included in the output. The default is FALSE.

verbosity

integer value indicating the amount of messages to display; currently meaningful values are 0, 1, and 2 (the default).

...

additional arguments to be passed to modelTrim (if step = TRUE and criterion = "significance").

Details

Trend Surface Analysis is a way to model the spatial structure in species' distributions by regressing occurrence data on the spatial coordinates x and y, for a linear trend, or on polynomial terms of these coordinates (x^2, y^2, x*y, etc.), for curvilinear trends (Legendre & Legendre, 1998; Borcard et al., 2011). Second- and third-degree polynomials are often used. 'multTSA' allows specifying the degree of the spatial polynomial to use. By default, it uses a 3rd-degree polynomial and performs stepwise AIC selection of the polynomial terms to include.

Value

This function returns a matrix or data frame containing the identifier column (if provided in 'id.col') and one column per species containing the value predicted by the trend surface analysis. If save.models = TRUE, the output is a list containing this dataframe plus a list of the model objects.

Author(s)

A. Marcia Barbosa

References

Borcard D., Gillet F. & Legendre P. (2011) Numerical Ecology with R. Springer, New York.

Legendre P. & Legendre L. (1998) Numerical Ecology. Elsevier, Amsterdam.

See Also

distPres, poly, multGLM

Examples

data(rotif.env)

head(rotif.env)

names(rotif.env)

tsa <- multTSA(rotif.env, sp.cols = 18:20, 
coord.cols = c("Longitude", "Latitude"), id.col = 1)

head(tsa)

fuzzySim documentation built on Oct. 31, 2022, 1:07 a.m.