Compute Conditional Probability of Each Observed Outcome Given Each True Outcome, for Every Subject
pistar_compute(gamma, Z, n, n_cat)pistar_compute returns a matrix of conditional probabilities,
\(P(Y_i^* = k | Y_i = j, Z_i) = \frac{\text{exp}\{\gamma_{kj0} + \gamma_{kjZ} Z_i\}}{1 + \text{exp}\{\gamma_{kj0} + \gamma_{kjZ} Z_i\}}\)
for each of the \(i = 1, \dots,\)
n subjects. Rows of the matrix
correspond to each subject and observed outcome. Specifically, the probability
for subject \(i\) and observed category $1$ occurs at row \(i\). The probability
for subject \(i\) and observed category $2$ occurs at row \(i +\)
n.
Columns of the matrix correspond to the true outcome categories \(j = 1, \dots,\)
n_cat.
A numeric matrix of regression parameters for the observed
outcome mechanism, Y* | Y
(observed outcome, given the true outcome) ~ Z (misclassification
predictor matrix). Rows of the matrix correspond to parameters for the Y* = 1
observed outcome, with the dimensions of Z.
Columns of the matrix correspond to the true outcome categories
\(j = 1, \dots,\) n_cat.
A numeric design matrix.
An integer value specifying the number of observations in the sample.
This value should be equal to the number of rows of the design matrix, Z.
The number of categorical values that the true outcome, Y,
and the observed outcome, Y* can take.