Restriction for multiple dependent and independent variables

Usage

rROC(x, ...)

x

data.frame: See rROC.data.frame. data.frame containing all dependent and independent variables as columns. Dependent/independent variable column names must be given as "dependent_vars"/"independent_vars" arguments.
matrix: See rROC.matrix. Matrix of (samples x features). Dependent variable(s) must be given as "y" argument.
numeric vector: See rROC.numeric. Numeric vector of independent variable. Dependent variable(s) must be given as "y" argument.

...

y

Either a vector of dependent variable values or a list of length 1 of a vector of dependent variable values. If NULL, dependent_vars must be given.

save_path

Path to save the results to. Intermediate results are saved into the directory file.path(save_path, "_partial_directory").

save_intermediate

Should intermediate results be saved to disk? If TRUE, every combination by itself is saved into file.path(save_path, "_partial_directory").

load_existing_intermediate

Should the earlier saved intermediate results in the folder file.path(save_path, "_partial_directory") be loaded?

do_plots

Should the plot_density_rROC_empirical be calculated and returned?

verbose

Should progress be printed?

n_permutations

How many permutations should be done

fix_seed

boolean: If not FALSE, the seed for each permutation will be set by set.seed(fix_seed + permutation_i)

parallel_permutations

boolean: If TRUE, the permutation will be done via future.apply::future_lapply, otherwise by base::lapply

positive_label

Label for the positive class. All other values of response are regarded as negative cases.

return_proc

Should pROC::roc() be returned for the full dataset? 2) Should pROC::roc() be returned on each of the part datasets? Only works with get_all_aucs_fun=get_all_aucs after get_all_aucs_norecalculation() does not calculate the ROC curves for each restriction separately.

A list of lists of simple_rROC_permutation and plot results. It is structured as follows: