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