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

### Description

Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subject

### Usage

```
pitilde_compute_for_chains(chain_colMeans, V, n, n_cat)
```

### Arguments

`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, |

### Value

`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`

.

*COMBO*version 1.1.0 Index]