predict.zlm {BMS} | R Documentation |

## Predict Method for zlm Linear Model

### Description

Expected value (And standard errors) of predictions based on 'zlm' linear Bayesian model under Zellner's g prior

### Usage

```
## S3 method for class 'zlm'
predict(object, newdata = NULL, se.fit = FALSE, ...)
```

### Arguments

`object` |
a zlm linear model object - see |

`newdata` |
An optional data.frame, matrix or vector containing variables with which to predict. If omitted, then (the expected values of) the fitted values are returned. |

`se.fit` |
A switch indicating if the standard deviations for the predicted varaibles are required. |

`...` |
further arguments passed to or from other methods. |

### Value

A vector with (expected values of) fitted values.

If
`se.fit`

is `TRUE`

, then the output is a list with the following
elements:

`fit` |
a vector with the expected values of fitted values |

`std.err` |
a vector with the standard deviations of fitted values |

`se.fit` |
a vector with the standard errors without the residual scale
akin to |

`residual.scale` |
The part from the standard deviations that involves the identity matrix.
Note that |

### See Also

`bms`

for creating zlm objects,
`predict.lm`

for a comparable function,
`predict.bma`

for predicting with bma objects

Check http://bms.zeugner.eu for additional help.

### Examples

```
data(datafls)
mm=zlm(datafls,g="EBL")
predict(mm) #fitted values
predict(mm, newdata=1:41) #prediction based on a 'new data point'
#prediction based on a 'new data point', with 'standard errors'
predict(mm, newdata=datafls[1,], se.fit=TRUE)
```

*BMS*version 0.3.5 Index]