LogisticRegression {ILoReg} | R Documentation

## Clustering projection using logistic regression from the LiblineaR R package

### Description

The function implements a script that downsamples data a dataset, trains a logistic regression classifier model and then projects its clustering onto itself using a trained L1-regularized logistic regression model.

### Usage

    LogisticRegression(
      training.sparse.matrix = NULL,
      training.ident = NULL,
      C = 0.3,
      reg.type = "L1",
      test.sparse.matrix = NULL,
      d = 0.3
    )

### Arguments

`training.sparse.matrix` | A sparse matrix (dgCMatrix) containing training sample's gene expression data with genes in rows and cells in columns. Default is `NULL`.
`training.ident` | A named factor containing sample's cluster labels for each cell in training.sparse.matrix. Default is `NULL`.
`C` | Cost of constraints violation in L1-regularized logistic regression (C). Default is `0.3`.
`reg.type` | "L1" for LASSO and "L2" for Ridge. Default is "L1".
`test.sparse.matrix` | A sparse matrix (dgCMatrix) containing test sample's gene expression data with genes in rows and cells in columns. Default is `NULL`.
`d` | A numeric smaller than `1` and greater than `0` that determines how many cells per cluster should be down- and oversampled (d in N/k*d), where N is the total number of cells and k the number of clusters. Default is `0.3`.

### Value

a list containing the output of the LiblineaR prediction

[Package ILoReg version 1.0.0 Index]