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 |