run_cv: Run the bayesian model with spatial cross validation

In forestecology: Fitting and Assessing Neighborhood Models of the Effect of Interspecific Competition on the Growth of Trees

Description

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.

Usage

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
)

forestecology documentation built on Oct. 2, 2021, 5:07 p.m.