lps.bma {BMS} | R Documentation |

## Log Predictive Score

### Description

Computes the Log Predictive Score to evaluate a forecast based on a bma object

### Usage

```
lps.bma(object, realized.y, newdata = NULL)
```

### Arguments

`object` |
an object of class |

`realized.y` |
a vector with realized values of the dependent variables
to be plotted in addition to the predictive density, must have its length
conforming to |

`newdata` |
Needs to be provided if |

### Details

The log predictive score is an indicator for the likelihood of several
forecasts.

It is defined as minus the arithmethic mean of the logarithms
of the point densities for `realized.y`

given `newdata`

.

Note
that in most cases is more efficient to first compute the predictive density
object via a call to `pred.density`

and only then pass the
result on to `lps.bma`

.

### Value

A scalar denoting the log predictive score

### See Also

`pred.density`

for constructing predictive densities,
`bms`

for creating `bma`

objects, `density.bma`

for plotting coefficient densities

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

### Examples

```
data(datafls)
mm=bms(datafls,user.int=FALSE,nmodel=100)
#LPS for actual values under the used data (static forecast)
lps.bma(mm, realized.y=datafls[,1] , newdata=datafls[,-1])
#the same result via predicitve.density
pd=pred.density(mm, newdata=datafls[,-1])
lps.bma(pd,realized.y=datafls[,1])
# similarly for a linear model (not BMA)
zz = zlm(datafls)
lps.bma(zz, realized.y=datafls[,1] , newdata=datafls[,-1])
```

*BMS*version 0.3.5 Index]