pistar_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

```
pistar_compute_for_chains(chain_colMeans, Z, n, n_cat)
```

### Arguments

`chain_colMeans` |
A numeric vector containing the posterior means for all
sampled parameters in a given MCMC chain. |

`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, |

`n_cat` |
The number of categorical values that the true outcome, |

### Value

`pistar_compute_for_chains`

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 $0$ occurs at row `i`

. The probability
for subject `i`

and observed category $1$ occurs at row `i +`

`n`

.
Columns of the matrix correspond to the true outcome categories `j = 1, \dots,`

`n_cat`

.

*COMBO*version 1.1.0 Index]