| lps.bma {BMS} | R Documentation |
Computes the Log Predictive Score to evaluate a forecast based on a bma object
lps.bma(object, realized.y, newdata = NULL)
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. |
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.
A scalar denoting the log predictive score
Martin Feldkircher and Stefan Zeugner
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.
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])