exp_max_algorithm: Fit a random forest using an EM algorithm

In lorecatta/DENVfoiMap: Runs the analysis of the paper on global dengue FOI mapping and prediction of intervention impact

Fit a random forest using an EM algorithm

Description

The function fits a random forest model to FOI data point estimates at 1/6 degree resolution using an Expectation Maximization algorithm

Usage

exp_max_algorithm(parms, adm_dataset, pxl_dataset, covariates_names,
  RF_obj_path = NULL, RF_obj_name = NULL, train_dts_path = NULL,
  train_dts_name = NULL, adm_covariates = NULL)

Arguments

parms	list of user-defined parameters.
adm_dataset	dataframe of the bootstrapped dataset of foi estimates and covariates at admin unit 1 resolution (pre-processed).
pxl_dataset	dataframe of the bootstrapped dataset of foi estimates and covariates at 1/6 degree resolution.
covariates_names	character vector of covariates names.
RF_obj_path	character string of the directory for saving the random forest object model. Default = NULL.
RF_obj_name	character of name of the random forest model object to save. Default = NULL.
train_dts_path	character string of the directory for saving the training dataset. Default = NULL.
train_dts_name	character of name of the training dataset dataframe to save. Default = NULL.
adm_covariates	dataframe of global covariates at admin unit 1 resolution. Default = NULL.

Value

the random forest model object returned by ranger.

lorecatta/DENVfoiMap documentation built on April 1, 2022, 4:05 p.m.