logit_model: the individualized binary logistic regression for categorical...

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

Description

logit_model fit the categorical data by the individualized binary logistic regression

Usage

logit_model(splitted, newY)

Arguments

splitted	A list containing the datasets which we will use in the categorical case. Note that the element of the splitted is the collections of samples from Classes 0 and Classes k.
newY	A numeric number denotes the value of the labels from 0 to K which is the number of categories

Details

logit_model fits the splitted data by using the the individualized binary logistic regression according to the value of newY. Because we use use Class 0 as the baseline for modeling the probability ratio of Class k to Class 0 by fitting K individual logistic models, if newY equal to 0, it means we need fit all elements of the splitted data. Otherwise, we only fit the samples from class 0 and class newY.

Value

beta_mat

a matrix contains the estimated coefficient. Note that the beta_mat is a n * p matrix which n is the number of the explanatory variables and p+1 is the number of categories

References

Li, J., Chen, Z., Wang, Z., & Chang, Y. I. (2020). Active learning in multiple-class classification problems via individualized binary models. Computational Statistics & Data Analysis, 145, 106911. doi:10.1016/j.csda.2020.106911

See Also

logit_model_ord for ordinal case.

Examples

## For an example, see example(seq_cat_model)

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