pred.density {BMS}  R Documentation 
Predictive densities for conditional forecasts
pred.density(object, newdata = NULL, n = 300, hnbsteps = 30, ...)
object 

newdata 
A data.frame, matrix or vector containing variables with which to predict. 
n 
The integer number of equally spaced points at which the density is to be estimated. 
hnbsteps 
The number of numerical integration steps to be used in case
of a hyperg prior (cf. argument 
... 
arguments to be passed on to 
The predictive density is a mixture density based on the nmodels
best
models in a bma
object (cf. nmodel
in bms
).
The number of 'best models' to retain is therefore vital and should be set
quite high for accuracy.
pred.density
returns a list of class pred.density
with
the following elements
densities() 
a list whose elements each contain the estimated density for each forecasted observation 
fit 
a vector with the expected values of the predictions (the 'point forecasts') 
std.err 
a vector with the standard deviations of the predictions (the 'standard errors') 
dyf(realized.y, predict_index=NULL) 
Returns the
densities of realized response variables provided in 
lps(realized.y, predict_index=NULL) 
Computes the log predictive score
for the response varaible provided in 
plot((x, predict_index =
NULL, addons = "eslz", realized.y = NULL, addons.lwd = 1.5, ...) 
the same
as 
n 
The number of equally spaced
points for which the density (under 
nmodel 
The number of best models predictive densities are based upon. 
call 
the call that created this 
In BMS version 0.3.0, pred.density
may only cope with builtin
gprior
s, not with any userdefined priors.
predict.bma
for simple point forecasts,
plot.pred.density
for plotting predictive densities,
lps.bma
for calculating the log predictive score
independently, quantile.pred.density
for extracting quantiles
Check http://bms.zeugner.eu for additional help.
data(datafls) mm=bms(datafls,user.int=FALSE) #predictive densityfor two 'new' data points pd=pred.density(mm,newdata=datafls[1:2,]) #fitted values based on best models, same as predict(mm, exact=TRUE) pd$fit #plot the density for the first forecast observation plot(pd,1) # the same plot ' naked' plot(pd$densities()[[1]]) #predict density for the first forecast observation if the dep. variable is 0 pd$dyf(0,1) #predict densities for both forecasts for the realizations 0 and 0.5 pd$dyf(rbind(c(0,.5),c(0,.5))) # calc. Log Predictive Score if both forecasts are realized at 0: lps.bma(pd,c(0,0))