hypervolume_n_resample: Bootstrap n hypervolumes

In hypervolume: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls

Bootstrap n hypervolumes

Description

The function hypervolume_n_resample() generates n hypervolumes using data bootstrapped from original data of the input hypervolumes.

Usage

hypervolume_n_resample(name,
                       hv_list,
                       n = 10,
                       points_per_resample = 'sample_size',
                       cores = 1,
                       verbose = TRUE,
                       seed = NULL)

Arguments

name	File name; The function writes hypervolumes to file in "./Objects/<name>""
hv_list	A Hypervolume or HypervolumeList object.
n	Number of resamples to take. Used for every method.
points_per_resample	Number of points in each resample. If the input is sample_size, then the same number of points as the original sample is used.
cores	Number of logical cores to use while generating bootstraped hypervolumes. If parallel backend already registered to doParallel, function will use that backend and ignore the argument in cores.
verbose	Logical value; If function is being run sequentially, outputs progress bar in console.
seed	Set seed for random number generation.

Details

hypervolume_n_resample() creates a directory called Objects in the current working directory if a directory of that name doesn't already exist. A directory is then created for each hypervolume in hv_list. Returns an absolute path to directory with resampled hypervolumes.
It is possible to access the hypervolumes by using readRDS to read the hypervolume objects one by one.
The resampled hypervolumes are generated using the same parameters used to generate the input hypervolume. The only exception is that the bandwidth is re-estimated if method = "gaussian" or method = "box". See copy_param_hypervolume for more details.

Value

Returns a string containing an absolute path equivalent to ./Objects/<name>

See Also

hypervolume_n_occupancy_bootstrap

Examples

## Not run: 

library(palmerpenguins)
data(penguins)
bill_data = na.omit(penguins[,3:4])
hv = hypervolume(bill_data)

# Example 1: get 50 resampled hypervolumes for each input hypervolume
# Use detectCores to see how many cores are availible in current environment
# Set cores = 1 to run sequentially (default)
# bootstrap the hypervolumes
hv_list_boot = hypervolume_n_resample(name = "example", hv_list, n = 50)


## End(Not run)

hypervolume documentation built on Sept. 14, 2023, 5:08 p.m.