part_random: Conventional data partitioning methods

In sjevelazco/flexsdm: Tools for Data Preparation, Fitting, Prediction, Evaluation, and Post-Processing of Species Distribution Models

Conventional data partitioning methods

Description

This function provides different conventional (randomized, non-spatial) partitioning methods based on cross validation folds (kfold, rep_kfold, and loocv), as well as bootstrap (boot)

Usage

part_random(data, pr_ab, method = NULL)

Arguments

data	data.frame. Database with presences, presence-absence, or pseudo-absence, records for a given species
pr_ab	character. Column name of "data" with presences, presence-absence, or pseudo-absence. Presences must be represented by 1 and absences by 0
method	character. Vector with data partitioning method to be used. Usage part=c(method= 'kfold', folds='5'). Methods include: kfold: Random partitioning into k-folds for cross-validation. 'folds' refers to the number of folds for data partitioning, it assumes value >=1. Usage method = c(method = "kfold", folds = 10). rep_kfold: Random partitioning into repeated k-folds for cross-validation. Usage method = c(method = "rep_kfold", folds = 10, replicates=10). 'folds' refers to the number of folds for data partitioning, it assumes value >=1. 'replicate' refers to the number of replicates, it assumes a value >=1. loocv: Leave-one-out cross-validation (a.k.a. Jackknife). It is a special case of k-fold cross validation where the number of partitions is equal to the number of records. Usage method = c(method = "loocv"). boot: Random bootstrap partitioning. Usage method=c(method='boot', replicates='2', proportion='0.7'). 'replicate' refers to the number of replicates, it assumes a value >=1. 'proportion' refers to the proportion of occurrences used for model fitting, it assumes a value >0 and <=1. In this example proportion='0.7' mean that 70% of data will be used for model training, while 30% will be used for model testing.

Value

A tibble object with information used in the 'data' argument and additional columns named .part containing the partition groups. The rep_kfold and boot method will return as many ".part" columns as replicated defined. For the rest of the methods, a single .part column is returned. For kfold, rep_kfold, and loocv partition methods, groups are defined by integers. In contrast, for boot method, the partition groups are defined by the characters 'train' and 'test'.

References

• Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38-49. https://doi.org/10.1017/S0376892997000088

See Also

part_sblock, part_senv, sample_pseudoabs, sample_background

Examples

## Not run: 
data("abies")
abies$partition <- NULL
abies <- tibble(abies)

# K-fold method
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 10)
)
abies2

# Repeated K-fold method
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "rep_kfold", folds = 10, replicates = 10)
)
abies2

# Leave-one-out cross-validation (loocv) method
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "loocv")
)
abies2

# Bootstrap method
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "boot", replicates = 50, proportion = 0.7)
)
abies2
abies2$.part1 %>% table() # Note that for this method .partX columns have train and test words.

## End(Not run)

sjevelazco/flexsdm documentation built on Feb. 28, 2025, 9:07 a.m.