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seqest (version 1.0.1)

Sequential Method for Classification and Generalized Estimating Equations Problem

Description

Sequential method to solve the the binary classification problem by Wang (2019) , multi-class classification problem by Li (2020) and the highly stratified multiple-response problem by Chen (2019) .

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Version

Install

install.packages('seqest')

Monthly Downloads

26

Version

1.0.1

License

GPL (>= 2)

Maintainer

Xiaoba Pan

Last Published

June 17th, 2020

Functions in seqest (1.0.1)

ase_seq_logit

variable selection and stopping criterion
D_optimal

Get the most informative subjects for the clustered data
genCorMat

Generate the correlation matrix for the clusteded data
gen_bin_data

generate the data used for the model experiment
A_optimal_ord

Get the most informative subjects from unlabeled dataset for the ordinal case
logit_model

the individualized binary logistic regression for categorical response data.
getWH_ord

Get the matrices W and H for the ordinal case
gen_multi_data

Generate the training data and testing data for the categorical and ordinal case.
getWH

Get the matrices W and H for the categorical case
logit_model_ord

the individualized binary logistic regression for ordinal response data.
seq_ord_model

The sequential logistic regression model for multi-classification problem under the ordinal case.
print.seqGEE

Print the results by the generalized estimating equations.
QIC

Calculate quasi-likelihood under the independence model criterion (QIC) for Generalized Estimating Equations.
print.seqbin

Print the results by the binary logistic regression model
seq_bin_model

The sequential logistic regression model for binary classification problem.
seq_GEE_model

The The sequential method for generalized estimating equations case.
print.seqmulti

Print the results by the multi-logistic regression model
init_multi_data

Generate the labeled and unlabeled datasets
update_data_ord

Add the new sample into labeled dataset from unlabeled dataset for the ordinal case
is_stop_ASE

Determining whether to stop choosing sample
seq_cat_model

The sequential logistic regression model for multi-classification problem under the categorical case.
update_data_cat

Add the new sample into labeled dataset from unlabeled dataset for the categorical case
getMH

Get the matrices M and H for the clustered data for the GEE case
evaluateGEEModel

The adaptive shrinkage estimate for generalized estimating equations
genBin

Generate the correlated binary response data for discrete case
A_optimal_cat

Get the most informative subjects from unlabeled dataset for the categorical case
gen_GEE_data

Generate the datasets with clusters