Sequential Method for Classification and Generalized Estimating Equations Problem

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Documentation for package ‘seqest’ version 1.0.1

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ase_seq_logit variable selection and stopping criterion
A_optimal_cat Get the most informative subjects from unlabeled dataset for the categorical case
A_optimal_ord Get the most informative subjects from unlabeled dataset for the ordinal case
D_optimal Get the most informative subjects for the clustered data
evaluateGEEModel The adaptive shrinkage estimate for generalized estimating equations
genBin Generate the correlated binary response data for discrete case
genCorMat Generate the correlation matrix for the clusteded data
gen_bin_data generate the data used for the model experiment
gen_GEE_data Generate the datasets with clusters
gen_multi_data Generate the training data and testing data for the categorical and ordinal case.
getMH Get the matrices M and H for the clustered data for the GEE case
getWH Get the matrices W and H for the categorical case
getWH_ord Get the matrices W and H for the ordinal case
init_multi_data Generate the labeled and unlabeled datasets
is_stop_ASE Determining whether to stop choosing sample
logit_model the individualized binary logistic regression for categorical response data.
logit_model_ord the individualized binary logistic regression for ordinal response data.
print.seqbin Print the results by the binary logistic regression model
print.seqGEE Print the results by the generalized estimating equations.
print.seqmulti Print the results by the multi-logistic regression model
QIC Calculate quasi-likelihood under the independence model criterion (QIC) for Generalized Estimating Equations.
seq_bin_model The sequential logistic regression model for binary classification problem.
seq_cat_model The sequential logistic regression model for multi-classification problem under the categorical case.
seq_GEE_model The The sequential method for generalized estimating equations case.
seq_ord_model The sequential logistic regression model for multi-classification problem under the ordinal case.
update_data_cat Add the new sample into labeled dataset from unlabeled dataset for the categorical case
update_data_ord Add the new sample into labeled dataset from unlabeled dataset for the ordinal case