hypervolume_n_occupancy_permute: Hypervolumes through permuting labels of n pairwise groups of...

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

Hypervolumes through permuting labels of n pairwise groups of hypervolumes

Description

Permute labels of an hypervolume_n_occupancy() object and calculate hypervolume_n_occupancy() for the permuted objects. This function is meant for taking a sample of all permutations and does not guarantee that permutations are not repeated. Newly generated hypervolume objects are written to file. This function is to be used within the occupancy routine.

Usage

hypervolume_n_occupancy_permute(name, 
                                hv_list1,
                                hv_list2,
                                verbose = TRUE,
                                n = 9,
                                cores = 1)

Arguments

name	File name; The function writes hypervolumes to file in ./Objects/<name>
hv_list1	An hypervolume list generated with hypervolume_n_occupancy().
hv_list2	The hypervolume list used to generate hv_list1.
verbose	Logical value; outputs progress bar in console.
n	Number of permutations to take.
cores	Number of logical cores to use while generating permuted hypervolumes. If parallel backend already registered to doParallel, function will use that backend and ignore the argument in cores.

Details

hypervolume_n_occupancy_permute() creates a directory called Objects in the current working directory if a directory of that name doesn't already exist. Within this directory, it creates a directory for each pairwise combinations of elements within hv_list1. Group labels are permuted and a new HypervolumeList is saved as a rds file for each pairwise combination. IMPORTANT: only group labels are permuted, random points are kept fixed and will be the same across all the permuted hypervolumes.

hypervolume_n_occupancy_test() takes in a hypervolume_n_occupuancy_permute() filepath output.

It is also possible to access the hypervolumes by using readRDS to read the hypervolume objects in one by one.

Value

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

Warning

hypervolume_n_occupancy_permute() requires a lot of disk space especially when building occupancy hypervolumes with method = "box". Try with a small number of replications and check the folder Objects for memory usage before to proceed.

See Also

hypervolume_n_occupancy

Examples

## Not run: 
data(penguins,package='palmerpenguins')
penguins_no_na = as.data.frame(na.omit(penguins))


# split the dataset on species and sex
penguins_no_na_split = split(penguins_no_na, 
                        paste(penguins_no_na$species, penguins_no_na$sex, sep = "_"))


# calculate the hypervolume for each element of the splitted dataset
hv_list = mapply(function(x, y) 
  hypervolume_gaussian(x[, c("bill_length_mm", "flipper_length_mm")],
                       samples.per.point=100, name = y), 
  x = penguins_no_na_split, 
  y = names(penguins_no_na_split))
  

names(hv_list) <- names(penguins_no_na_split)
hv_list <- hypervolume_join(hv_list)


hv_occupancy_list_sex <- hypervolume_n_occupancy(hv_list, 
                              classification = rep(c("female", "male"), each = 3))

# takes 9 permutations on 1 core
hypervolume_n_occupancy_permute("permute", hv_occupancy_list_sex,
                                hv_list , n = 9, cores = 1)




## End(Not run)

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