tenm (version 0.5.1)

cov_center: Function to compute the covariance matrix of an ellipsoid niche model.

Description

Computes the covariance matrix, niche centroid, volume, and other ellipsoid parameter based on the values of niche variables from occurrence points.

Usage

cov_center(data, mve = TRUE, level, vars = NULL)

Value

A list containing the following components:

• centroid: Centroid (mean vector) of the ellipsoid.

• covariance_matrix: Covariance matrix based on the input data.

• volume: Volume of the ellipsoid.

• semi_axes_lengths: Lengths of semi-axes of the ellipsoid.

• axis_coordinates: Coordinates of ellipsoid axes.

Arguments

data

A data.frame or matrix containing numeric values of variables used to model the niche.

mve

Logical. If TRUE, computes a minimum volume ellipsoid using the cov.mve function from the MASS package. If FALSE, uses the covariance matrix of the input data.

level

Proportion of data to be used for computing the ellipsoid, applicable when mve is TRUE.

vars

Vector specifying column indexes or names of variables in the input data used to fit the ellipsoid model.

Examples

# \donttest{
library(tenm)
data("abronia")
tempora_layers_dir <- system.file("extdata/bio",package = "tenm")
abt <- tenm::sp_temporal_data(occs = abronia,
                              longitude = "decimalLongitude",
                              latitude = "decimalLatitude",
                              sp_date_var = "year",
                              occ_date_format="y",
                              layers_date_format= "y",
                              layers_by_date_dir = tempora_layers_dir,
                              layers_ext="*.tif$")
abtc <- tenm::clean_dup_by_date(abt,threshold = 10/60)
future::plan("multisession",workers=2)
abex <- tenm::ex_by_date(abtc,train_prop=0.7)
future::plan("sequential")
mod <- tenm::cov_center(data = abex$env_data,
                        mve = TRUE,
                        level = 0.975,
                        vars = c("bio_05","bio_06","bio_12"))
# Print model parameters
print(mod)
# }