Compute the Mean Conditional Probability of Second-Stage Correct Classification, by First-Stage and True Outcome Across all Subjects for each MCMC Chain
pitilde_by_chain(n_chains, chains_list, V, n, n_cat)pitilde_by_chain returns a numeric matrix of the average
conditional probability \(P( \tilde{Y} = j | Y^* = j, Y = j, V)\) across all subjects for
each MCMC chain. Rows of the matrix correspond to MCMC chains, up to n_chains.
The first column contains the conditional probability \(P( \tilde{Y} = 1 | Y^* = 1, Y = 1, V)\).
The second column contains the conditional probability \(P( \tilde{Y} = 2 | Y^* = 2, Y = 2, V)\).
An integer specifying the number of MCMC chains to compute over.
A numeric list containing the samples from n_chains
MCMC chains.
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, V.
The number of categorical values that the true outcome, \(Y\), the first-stage observed outcome, \(Y*\), and the second-stage observed outcome, \(\tilde{Y}\), can take.