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

Description Usage Arguments Details Value References See Also Examples

View source: R/logit_model_cat.R


logit_model fit the categorical data by the individualized binary logistic regression


logit_model(splitted, newY)



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.


A numeric number denotes the value of the labels from 0 to K which is the number of categories


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.



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


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.


## For an example, see example(seq_cat_model)

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

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