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

Source: R/cov_center.R
cov_center.Rd

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)

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.

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.

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)
#> $centroid
#>     bio_05     bio_06     bio_12 
#>  217.10256   54.69231 1106.07692 
#> 
#> $covariance
#>           bio_05   bio_06      bio_12
#> bio_05  731.3050 456.5061   -249.8502
#> bio_06  456.5061 418.6923    987.1559
#> bio_12 -249.8502 987.1559 152235.5992
#> 
#> $niche_volume
#> [1] 14127753
#> 
#> $SemiAxis_length
#>          a          b          c 
#>   28.44591   99.38665 1192.98935 
#> 
#> $axis_coordinates
#> $axis_coordinates[[1]]
#>         bio_05   bio_06   bio_12
#> vec_1 233.5857 31.50945 1106.254
#> vec_2 200.6194 77.87517 1105.899
#> 
#> $axis_coordinates[[2]]
#>         bio_05     bio_06   bio_12
#> vec_1 298.1029 112.282107 1105.835
#> vec_2 136.1022  -2.897492 1106.319
#> 
#> $axis_coordinates[[3]]
#>         bio_05   bio_06     bio_12
#> vec_1 215.1587 62.44309 2299.03951
#> vec_2 219.0465 46.94153  -86.88566
#> 
#> 
# }