| Title: | Calculate Variable Importance with Knock Off Variables |
|---|---|
| Description: | The variable importance is calculated using knock off variables. Then output can be provided in numerical and graphical form. Meredith L Wallace (2023) <doi:10.1186/s12874-023-01965-x>. |
| Authors: | Meredith Wallace [aut, cre]
|
| Maintainer: | Meredith Wallace <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0 |
| Built: | 2024-12-18 06:39:20 UTC |
| Source: | CRAN |
Calculate the variable importance of the domains for a given dataset
calc_vimps( dat, dep_var, doms, calc_ko = TRUE, calc_dom = FALSE, num_folds = 10, num_kos = 100, model_all = normal_model, model_subset = one_tree_model, mtry = NULL, min.node.size = NULL, iterations = 500, ko_path = NULL, results_path = NULL, output_file_ko = NULL, output_file_dom = NULL )calc_vimps( dat, dep_var, doms, calc_ko = TRUE, calc_dom = FALSE, num_folds = 10, num_kos = 100, model_all = normal_model, model_subset = one_tree_model, mtry = NULL, min.node.size = NULL, iterations = 500, ko_path = NULL, results_path = NULL, output_file_ko = NULL, output_file_dom = NULL )
dat |
A dataframe of data |
dep_var |
The dependent variable in the dat |
doms |
A dataframe of the variables in dat and the domain they belong to |
calc_ko |
True/False to calculate the knock_off importance |
calc_dom |
True/False to calculate the domain importance |
num_folds |
The number of folds to use while calculating the classification threshold for predictions |
num_kos |
The number of sets of knock off variables to create |
model_all |
The model to use in full ensemble mode in calculations |
model_subset |
The model to use sigularly for building ensembles from |
mtry |
The mtry value to use in the random forests |
min.node.size |
The min.node.size value to use in the random forests |
iterations |
Number of trees to build while calculating variable importance |
ko_path |
Where to store the knock off variable sets |
results_path |
Where to store the intermediary results for calculating variable importance |
output_file_ko |
Where to store the results of the knock off variable importance |
output_file_dom |
Where to store the results of the domain variable importance |
List with 1) Threshold for binary class labeling 2) Model metrics using all variables 3) Model metrics using knock-off variables 4) Variable importance with knock-offs
calc_vimps( data.frame( X1=c(2,8,3,9,1,4,3,8,0,9,2,8,3,9,1,4,3,8,0,9), X2=c(7,2,5,0,9,1,8,8,3,9,7,2,5,0,9,1,8,8,3,9), Y=c(0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1)), "Y", data.frame(domain=c('X1','X2'), variable=c('X1','X2')), num_folds=2, num_kos=1, iterations=50)calc_vimps( data.frame( X1=c(2,8,3,9,1,4,3,8,0,9,2,8,3,9,1,4,3,8,0,9), X2=c(7,2,5,0,9,1,8,8,3,9,7,2,5,0,9,1,8,8,3,9), Y=c(0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1)), "Y", data.frame(domain=c('X1','X2'), variable=c('X1','X2')), num_folds=2, num_kos=1, iterations=50)
Graph the variable importance results from calc_vimps
graph_results(results, object)graph_results(results, object)
results |
The results from calc_vimps |
object |
Which object from results to use for graphing results |
No return value