Compute E-step for Binary Outcome Misclassification Model Estimated With the EM-Algorithm
w_j(ystar_matrix, pistar_matrix, pi_matrix, sample_size, n_cat)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.
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.
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.
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.
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.
The number of categorical values that the true outcome, Y,
and the observed outcome, Y*, can take.