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

1 | ```
logit_model(splitted, newY)
``` |

`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 |

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.

`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 |

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_ord`

for ordinal case.

1 | ```
## For an example, see example(seq_cat_model)
``` |

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