w_j {COMBO} R Documentation

## Compute E-step for Binary Outcome Misclassification Model Estimated With the EM-Algorithm

### Description

Compute E-step for Binary Outcome Misclassification Model Estimated With the EM-Algorithm

### Usage

w_j(ystar_matrix, pistar_matrix, pi_matrix, sample_size, n_cat)


### Arguments

 ystar_matrix A numeric matrix of indicator variables (0, 1) for the observed outcome Y*. Rows of the matrix correspond to each subject. Columns of the matrix correspond to each observed outcome category. Each row should contain exactly one 0 entry and exactly one 1 entry. pistar_matrix A numeric matrix of conditional probabilities obtained from the internal function pistar_compute. Rows of the matrix correspond to each subject and to each observed outcome category. Columns of the matrix correspond to each true, latent outcome category. pi_matrix A numeric matrix of probabilities obtained from the internal function pi_compute. Rows of the matrix correspond to each subject. Columns of the matrix correspond to each true, latent outcome category. sample_size An integer value specifying the number of observations in the sample. This value should be equal to the number of rows of the observed outcome matrix, ystar_matrix. n_cat The number of categorical values that the true outcome, Y, and the observed outcome, Y*, can take.

### Value

w_j returns a matrix of E-step weights for the EM-algorithm, computed as follows: \sum_{k = 1}^2 \frac{y^*_{ik} \pi^*_{ikj} \pi_{ij}}{\sum_{\ell = 1}^2 \pi^*_{i k \ell} \pi_{i \ell}}. Rows of the matrix correspond to each subject. Columns of the matrix correspond to the true outcome categories j = 1, \dots, n_cat.

[Package COMBO version 1.0.0 Index]