predict.MxModel {OpenMx} | R Documentation |

`predict`

method for `MxModel`

objects`predict`

method for `MxModel`

objects

```
## S3 method for class 'MxModel'
predict(
object,
newdata = NULL,
interval = c("none", "confidence", "prediction"),
method = c("ML", "WeightedML", "Regression", "Kalman"),
level = 0.95,
type = c("latent", "observed"),
...
)
```

`object` |
an MxModel object from which predictions are desired |

`newdata` |
an optional |

`interval` |
character indicating what kind of intervals are desired. 'none' gives no intervals, 'confidence', gives confidence intervals, 'prediction' gives prediction intervals. |

`method` |
character the method used to create the predictions. See details. |

`level` |
the confidence or predictions level, ignored if not using intervals |

`type` |
character the type of thing you want predicted: latent variables or manifest variables. |

`...` |
further named arguments |

The `newdata`

argument is either a `data.frame`

or `MxData`

object. In the latter case is replaces the data in the top level model. In the former case, it is passed as the `observed`

argument of `mxData`

with `type='raw'`

and must accept the same further arguments as the data in the model passed in the `object`

argument.

The available methods for prediction are 'ML', 'WeightedML', 'Regression', and 'Kalman'. See the help page for `mxFactorScores`

for details on the first three of these. The 'Kalman' method uses the Kalman filter to create predictions for state space models.

[Package *OpenMx* version 2.21.11 Index]