R: 'predict' method for 'MxModel' objects

predict.MxModel {OpenMx} | R Documentation

## `predict` method for `MxModel` objects

### Description

`predict` method for `MxModel` objects

### Usage

    ## 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"),
      ...
    )

### Arguments

`object` | an MxModel object from which predictions are desired
`newdata` | an optional `data.frame` object. See details.
`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

### Details

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 2.19.5 Index]