View source: R/OmicSelector_load_datamix.R
OmicSelector_load_datamix | R Documentation |
This function loads the data created in preparation phase. It requires the output constructed by 'OmicSelector_prepare_split' function to be placed in working directory ('wd'), thus files 'mixed_train.csv', 'mixed_test.csv' and 'mixed_valid.csv' have to exist in the directory. For imbalanced data, the fuction can perform balancing using: 1. ROSE: https://journal.r-project.org/archive/2014/RJ-2014-008/RJ-2014-008.pdf - by default we generate 10 * number of cases in orginal dataset. 2. SMOTE (default): https://arxiv.org/abs/1106.1813 - by defult we use 'perc.under=100' and 'k=10'.
OmicSelector_load_datamix(
wd = getwd(),
smote_easy = T,
smote_over = 200,
use_smote_not_rose = T,
replace_smote = F,
selected_miRNAs = NULL,
class_interest = "Case",
remove_zero_var = T
)
wd |
Working directory with files for the loading. |
smote_easy |
Easy SMOTE (just SMOTE minority cases in the amount of the difference between minority and majority classes). If set to TRUE smote_over has no meaning. Please not that no undersampling of majority class is performed in this method, so we consider it the best for small datasets. |
smote_over |
Oversampling of minority class in SMOTE function (deterimes the number of cases in final dataset). See 'perc.over' in 'DMwR::SMOTE()“ function. |
use_smote_not_rose |
Set TRUE for SMOTE instead of ROSE. |
replace_smote |
For some analyses we may want to replace imbalanced train dataset with balanced dataset. This saved coding time in some functions. |
selected_miRNAs |
If null - take all features staring with "hsa", if set - vector of feature names to be selected. |
class_interest |
Value of variable "Class" used in the cases of interest. Default: "Case". Other values in variable Class will be used as controls and encoded as "Control". |
remove_zero_var |
Remove features with zero variance. |
The list of objects in the following order: train, test, valid, train_smoted, trainx, trainx_smoted, merged. (trainx contains only the miRNA data without metadata)
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