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 `pred.density`, or class `bma` (cf. `bms`), or class `zlm` `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` `newdata` Needs to be provided if `object` is not of class `pred.density`: a data.frame, matrix or vector containing variables with which to predict.

### Details

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

### Author(s)

Martin Feldkircher and Stefan Zeugner

`pred.density` for constructing predictive densities, `bms` for creating `bma` objects, `density.bma` for plotting coefficient densities

### 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])

```

[Package BMS version 0.3.1 Index]