| logit_model {seqest} | R Documentation |
the individualized binary logistic regression for categorical response data.
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)