A_optimal_cat: Get the most informative subjects from unlabeled dataset for...

In seqest: Sequential Method for Classification and Generalized Estimating Equations Problem

Description

Get the most informative subjects from unlabeled dataset under the categorical case.

Usage

A_optimal_cat(X, beta, W, unlabeledIDs)

Arguments

X	A matrix containing all the samples except their labels including the labeled samples and the unlabeled samples.
beta	A matrix contains the estimated coefficient. Note that the beta is a n * k matrix which n is the number of the explanatory variables and k+1 is the number of categories
W	A matrix denotes the inverse information matrix of the coefficient beta.
unlabeledIDs	A numeric vector for the unique identification of the unlabeled. dataset.

Details

A_optimal_cat uses the A optimality criterion from the experimental design to choose the most informative subjects under the the categorical case. We have obtained the variance-covariance matrix based on the current labeled samples which indicates how much information there is. Then we should repeatly calculate the information matrix after we choose a sample from the unlabeled dataset. Once we finish the iteration, we pick the sample which has the most information.

Value

a index of the most informative subjects from unlabeled dataset for the categorical case

seqest documentation built on July 2, 2020, 2:28 a.m.