aldvmm.gof {aldvmm} | R Documentation |

## Calculating Goodness of Fit Measures

### Description

`aldvmm.gof`

calculates residual- and likelihood-based goodness of fit measures.

### Usage

```
aldvmm.gof(res, par, ll)
```

### Arguments

`res` |
a numeric vector of residuals of all observations in the estimation data. |

`par` |
a named numeric vector of parameter estimates. |

`ll` |
a numeric value of the log-likelihood. |

### Details

`aldvmm.gof`

calculates mean squared errors as ```
MSE = \frac{\sum_{i = 1}^{n} (y_{i}
- \hat{y}_{i})^2}{n - k}
```

, and mean absolute
errors as ```
MAE = \frac{\sum_{i = 1}^{n} y_{i} - \hat{y}_{i}}{n -
k}
```

, where `y_{i}`

denotes observed
outcomes, `\hat{y}_{i}`

denotes fitted values, `n`

denotes the sample size, and `k`

denotes the number of parameters.
The Akaike information criterion is calculated as `2k - 2ll`

, and the Bayesian information criterion is calculated as
`k\log(n) - 2ll`

, where `ll`

denotes the
log-likelihood.

### Value

`aldvmm.gof`

returns a list including the following objects.

`mse` |
a numeric value of the mean squared error of observed versus fitted outcomes. |

`mae` |
a numeric value of the mean absolute error of observed versus fitted outcomes. |

`ll` |
a numeric value of the negative log-likelihood. |

`aic` |
a numeric value of the Akaike information criterion. |

`bic` |
a numeric value of the Bayesian information criterion. |

*aldvmm*version 0.8.8 Index]