CODEWITHSUNDEEP
spatialcross-validationtidymodels
Garrett Gimbel
Garrett Gimbel

Reputation: 45

jackknife (leave-one-out) cross validation with tidySDM and SpatialSample package in R

I am working with very low sample size (10-15) trying to create an ensemble SDM with the package TidySDM. Cross validation won't work with the random k fold method and in these cases of low sample size jackknife or leave one out cross validation is best practice.

TidySDM uses the spatial sample package and what i think is the jackknife function is "spatial_leave_location_out_cv". However it requires a "group" argument that i cannot figure out what to provide for this argument.

Right now I have a an sf dataframe with the following columns: Class - presence vs background locations. Geometry - the lat/lon location data (10010 rows in total,10 for the presences and 10000 for the background points). 7 predictor variables with the values extracted from the rasters

I have tried supplying the class and geometry columns for the group argument. The models failed to run with class as the argument. When i set geometry to the group argument, the models ran for about 8 hours and wasn't even half way through so I terminated the session.

What is the proper way to run this function for jackknife CV? Here is my code if it helps:

dive.cv <- spatial_leave_location_out_cv(data = dive.vars1, group = jackknife, v = NULL) 

autoplot(dive.cv)```

Upvotes: 0

Views: 21

Answers (1)

Andrea Manica
Andrea Manica

Reputation: 321

To run a remove-one jacknife with spatial_leave_location_out_cv, you will need to set up a grouping variable, such that each group contains a single presence and an appropriate number of background plots. Here is a simple reprex, using the lacerta dataset in tidysdm, subsetted to just 3 presences and 2 background points per presence.

library(tidysdm)
#> Loading required package: tidymodels
#> Loading required package: spatialsample
lacerta_thin <- readRDS(system.file("extdata/lacerta_thin_all_vars.rds",
                                    package = "tidysdm"))
########
# create a small dataset for the reprex
n_pres <- 3 # number of presences
n_bkg_per_pres <- 2 # number of background points per presence
set.seed(123)
lacerta_small <- rbind(lacerta_thin %>% filter(class == "presence") %>% 
                         sample_n(size = n_pres),
                          lacerta_thin %>% filter(class == "background") %>%
                         sample_n(size=n_pres * n_bkg_per_pres))
########
# now create groups, 1 per presence, each with 
# n_bkg_per_pres background points
lacerta_small$group <- NA
lacerta_small$group[lacerta_small$class == "presence"] <- 1:n_pres
lacerta_small$group[lacerta_small$class == "background"] <- 
  sample(rep(1:n_pres, each = n_bkg_per_pres), replace=FALSE)
########
# set up the folds for the jacknife
lacerta_cv <- spatial_leave_location_out_cv(data = lacerta_small,
                                            group = group) 
# confirm that we have the right balance of presence and background points
check_splits_balance(lacerta_cv, class)
#> # A tibble: 3 × 4
#>   presence_assessment background_assessment presence_analysis
#>                 <int>                 <int>             <int>
#> 1                   2                     4                 1
#> 2                   2                     4                 1
#> 3                   2                     4                 1
#> # ℹ 1 more variable: background_analysis <int>
autoplot(lacerta_cv)

Created on 2025-02-28 with reprex v2.1.1

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Upvotes: 0

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