Run the bayesian model with spatial cross validation

Source: R/model_functions.R

This function carries out the bayesian modeling process with spatial cross-validation as described in Allen and Kim (2020). Given a focal-competitor data frame, it appends a column with predicted growth values.

run_cv(
  focal_vs_comp,
  comp_dist,
  blocks,
  prior_param = NULL,
  run_shuffle = FALSE
)

Arguments

focal_vs_comp

data frame from create_focal_vs_comp()
comp_dist

Distance to determine which neighboring trees to a focal tree are competitors.
blocks

An sf object of a blockCV block output
prior_param

A list of {a_0, b_0, mu_0, V_0} prior hyperparameters. Defaults to a_0 = 250, b_0 = 250, mu_0 a vector of zeros of length \(p + 1\), V_0 an identity matrix of dimension \((p + 1) x (p + 1)\)
run_shuffle

boolean as to whether to run permutation test shuffle of competitor tree species within a particular focal_ID

Value

focal_vs_comp with new column of predicted growth_hat

See also

Other modeling functions: comp_bayes_lm(), create_bayes_lm_data(), predict.comp_bayes_lm()

Examples


run_cv(
  focal_vs_comp_ex,
  comp_dist = 1,
  blocks = blocks_ex
)

#> # A tibble: 6 × 8
#>   focal_ID focal_sp         dbh foldID    geometry growth comp            
#>      <dbl> <fct>          <dbl> <fct>      <POINT>  <dbl> <list>          
#> 1        2 american_beech    20 1        (1.5 2.5)  0.800 <tibble [2 × 4]>
#> 2        3 sugar_maple       15 1      (1.75 2.25)  1.00  <tibble [1 × 4]>
#> 3        4 american_beech    12 1          (3 1.5)  0.400 <tibble [1 × 4]>
#> 4        5 sugar_maple       35 1      (3.25 1.75)  1.40  <tibble [1 × 4]>
#> 5        7 sugar_maple       22 2          (8 1.5)  0.600 <tibble [3 × 4]>
#> 6        9 sugar_maple       42 2       (8.75 1.5)  1.40  <tibble [3 × 4]>
#> # … with 1 more variable: growth_hat <dbl>