# The sequential logistic regression model for multi-classification problem under the ordinal case. `seq_ord_model` chooses the subjects sequentially by the logistic regression model for ordinal case ```r seq_ord_model(labeled_ids, unlabeled_ids, splitted, newY, train, data, d = 0.8, adaptive = "random") ``` ## Arguments - `labeled_ids`: A numeric vector for the unique identification of the labeled dataset - `unlabeled_ids`: A numeric vector for the unique identification of the unlabeled dataset - `splitted`: A list containing the datasets which we will use in the cordinl case. Note that the element of the data_split is the samples from Classes k-1and Classes k - `newY`: A numeric number denotes the value of the labels from 0 to K which is the number of categories - `train`: A matrix for the labeled samples. Note that the indices of the samples in the train dataset is the same as the labeled_ids - `data`: A matrix denotes all the data including the labeled samples and the unlabeled samples. Note that the first column of the dataset is the response variable, that's the labels and the rest is the explanatory variables. - `d`: A numeric number specifying the length of the fixed size confidence set for our model. The default value is 0.8. - `adaptive`: A character string that determines the sample selection criterion to be used, matching one of 'random' or 'A_optimal'. The default value is 'random'. ## Returns a list containing the following components - **d**: the length of the fixed size confidence set that we specify - **n**: the current sample size when the stopping criterion is satisfied - **is_stopped**: the label of sequential iterations stop or not. When the value of is_stopped is TRUE, it means the iteration stops - **beta_est**: the estimated coeffificent when the criterion is safisfied - **cov**: the covariance matrix between the estimated parameters - **adaptive**: the sample selection criterion we used ## Details The `seq_ord_model` function and `seq_cat_model` function are very similar. seq_ord_model is also a multinomial logistic regression model but under the ordinal case that estimate the coefficient variables and determines the samples given the fixed size confidence set. seq_ord_model selects the sample in the same way as seq_cat_model: both are two methods. The details about the selecting methoed in seq_ord_model please refer to the `seq_cat_model` function. ## Examples ```r # generate the toy example beta <- matrix(c(1,2,1,-1,1,2), ncol=2) res <- gen_multi_data(beta, N = 10000, type = 'ord', test_ratio = 0.3) train_id <- res$train_id train <- res$train test <- res$test res <- init_multi_data(train_id, train, init_N = 300, type = 'ord') splitted <- res$splitted train <- res$train newY <- res$newY labeled_ids <- res$labeled_ids unlabeled_ids <- res$unlabeled_ids data <- res$data # use seq_ord_model to multi-classification problem under the ordinal case. # You can remove '#' to run the command. # start_time <- Sys.time() # logitA_ord <- seq_ord_model(labeled_ids, unlabeled_ids, splitted, newY, # train, data, d = 0.5, adaptive = "A_optimal") # logitA_ord$time <- as.numeric(Sys.time() - start_time, units = "mins") # print(logitA_ord) ``` ## 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 `seq_cat_model` for categorical case `seq_bin_model` for binary classification case `seq_GEE_model` for generalized estimating equations case.

seq_ord_model function