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`.
Usage
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
)Arguments
- 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.