pistar_compute function

Compute Conditional Probability of Each Observed Outcome Given Each True Outcome, for Every Subject

pistar_compute(gamma, Z, n, n_cat)

Arguments

• gamma: 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.

• Z: A numeric design matrix.

• n: 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.

• n_cat: The number of categorical values that the true outcome, Y, and the observed outcome, Y* can take.

Returns

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.