HybaseModel {BioM2}

Selection of the optimal base model

Description

Selection of the optimal base model

Usage

HybaseModel(
  data = NULL,
  pathlistDB = NULL,
  FeatureAnno = NULL,
  resampling = NULL,
  nfolds = 5,
  classifiers = "liblinear",
  predMode = "probability",
  PathwaySizeUp = 200,
  PathwaySizeDown = 20,
  MinfeatureNum_pathways = 10,
  Add_UnMapped = TRUE,
  Unmapped_num = 300,
  Add_FeartureSelection_Method = "wilcox.test",
  Inner_CV = TRUE,
  inner_folds = 10,
  Stage1_FeartureSelection_Method = "cor",
  cutoff = 0.3,
  Stage2_FeartureSelection_Method = "RemoveHighcor",
  cutoff2 = 0.95,
  cores = 1,
  verbose = TRUE
)

Arguments

data

The input training dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.

pathlistDB

A list of pathways with pathway IDs and their corresponding genes ('entrezID' is used). For details, please refer to ( data("GO2ALLEGS_BP") )

FeatureAnno

The annotation data stored in a data.frame for probe mapping. It must have at least two columns named 'ID' and 'entrezID'. (For details, please refer to data( data("MethylAnno") )

resampling

Resampling in mlr3verse.

nfolds

k-fold cross validation

classifiers

A string of character vectors(Learners in mlr3)

predMode

The prediction mode. Available options are c('probability', 'classification').

PathwaySizeUp

The upper-bound of the number of genes in each biological pathways.

PathwaySizeDown

The lower-bound of the number of genes in each biological pathways.

MinfeatureNum_pathways

The minimal defined pathway size after mapping your own data to pathlistDB(KEGG database/GO database).

Add_UnMapped

Whether to add unmapped probes for prediction

Unmapped_num

The number of unmapped probes

Add_FeartureSelection_Method

Feature selection methods.

Inner_CV

Whether to perform a k-fold verification on the training set.

inner_folds

k-fold verification on the training set.

Stage1_FeartureSelection_Method

Feature selection methods.

cutoff

The cutoff used for feature selection threshold. It can be any value between 0 and 1.

Stage2_FeartureSelection_Method

Feature selection methods.

cutoff2

The cutoff used for feature selection threshold. It can be any value between 0 and 1.

cores

The number of cores used for computation.

verbose

Whether to print running process information to the console

Value

A data frame containing the predictive performance of each basemodel