generate_training_test_data_wp: Generate training and prediction datasets for whole proteome

In ginnyintifa/PTMscape:

Description

This function generates training and prediction datasets for whole proteome to predict 1/n_fold of the proteome by the other n-1 fold data.

Usage

generate_training_test_data_wp(n_fold = 2, lower_bound = -1,
  upper_bound = 1, positive_feature_file, negative_feature_file, output_label)

Arguments

n_fold	Number of folds used for training and prediction, default set to 2
lower_bound	The lower bound of the scaled data range, default to -1.
upper_bound	The upper bound of the scaled data range, default to 1.
positive_feature_file	A Rds file containing the positive features(generated by g_feature_wp()).
negative_feature_file	A Rds file containing the negative features(generated by g_feature_wp()).
output_label	The string to tag the output files.

Details

This function outputs formatted feature files ready for Liblinear training and prediction.

Examples

generate_training_test_data_wp(n_fold = 2,
                        lower_bound = -1,
                        upper_bound = 1,
                        positive_feature_file = "ps_0103_noc_not_na_pos_feature.Rds",
                        negative_feature_file = "ps_0103_noc_not_na_candi_feature.Rds",
                        output_label = "ps_0103")

ginnyintifa/PTMscape documentation built on Nov. 9, 2021, 10:39 p.m.