# logit_model_ord: the individualized binary logistic regression for ordinal... In seqest: Sequential Method for Classification and Generalized Estimating Equations Problem

## Description

`logit_model_ord` fit the ordinal data by the individualized binary logistic regression

## Usage

 `1` ```logit_model_ord(splitted, newY, beta_mat) ```

## 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 `beta_mat` the initial estimate for the coefficient. Note that the values may be not accurate.

## Details

logit_model_ord fits the splitted data by using the the individualized binary logistic regression according to the value of newY. Under the ordinal case, we don't use the all training samples. Instead, we use two consecutive subgroups, such as Classes k - 1 and k , at a time for each individual model. Hence, we need fit the model acrroding to the value of newY. param splitted a list containing the datasets which we will use in the cordinl case. Note that the element of the splitted is the collections of samples from Classes 0k - 1and Classes k.

## 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

`logit_model` for categorical case.
 `1` ```## For an example, see example(seq_ord_model) ```