multiAdaSampling: multi Adaptive Sampling function

Description Usage Arguments Value Author(s) Examples

View source: R/multiAdaSampling.R

Description

Performs multiple Adaptive sampling to train a classifier model

Usage

1
multiAdaSampling(dat, lab)

Arguments

data

A dimension reduced matrix.

label

A vector of label information for each sample.

seed

Seed before base classifier model.

classifier

Base classifier model, either "SVM" or "RF"

percent

Percentage of samples to select at each iteration during.

L

Number of ensembles. Default to 10.

prob

logical flag to return sample's probabilities to each class.

balance

logical flag to down sample large cell types classes to the median of all class sizes.

iter

A number of iterations to perform adaSampling.

Value

A final prediction, probabilities for each cell type and the model are returned as a list.

Author(s)

Pengyi Yang

Examples

1
2
3
4
5
6
7
8
data("GSE87795_liver.development.data")

mat.expr = GSE87795_liver.development.data$data
cellTypes = GSE87795_liver.development.data$cellTypes

mat.pc = matPCs(mat.expr)

result = multiAdaSampling(mat.pc, cellTypes, seed = 1, classifier = "svm", percent = 1, L = 10)

SydneyBioX/scdney documentation built on Aug. 22, 2019, 10:55 a.m.