pitilde_compute_for_chains {COMBO} | R Documentation |
Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subject
pitilde_compute_for_chains(chain_colMeans, V, n, n_cat)
chain_colMeans |
A numeric vector containing the posterior means for all
sampled parameters in a given MCMC chain. |
V |
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, |
n_cat |
The number of categorical values that the true outcome, |
pitilde_compute_for_chains
returns a matrix of conditional probabilities,
P(\tilde{Y}_i = \ell | Y^*_i = k, Y_i = j, V_i) = \frac{\text{exp}\{\delta_{\ell kj0} + \delta_{\ell kjV} V_i\}}{1 + \text{exp}\{\delta_{\ell kj0} + \delta_{\ell kjV} V_i\}}
corresponding to each subject and observed outcome. Specifically, the probability
for subject i
and second-stage observed category $1$ occurs at row i
. The probability
for subject i
and second-stage observed category $2$ occurs at row i +
n
.
Columns of the matrix correspond to the first-stage outcome categories j = 1, \dots,
n_cat
.
The third dimension of the array corresponds to the true outcome categories,
j = 1, \dots,
n_cat
.