dlm.lpl {DGM} | R Documentation |
Calculate the log predictive likelihood for a specified set of parents and a fixed delta.
dlm.lpl(Yt, Ft, delta, priors = priors.spec())
Yt |
the vector of observed time series, length |
Ft |
the matrix of covariates, dim = number of thetas ( |
delta |
discount factor (scalar). |
priors |
list with prior hyperparameters. |
mt |
the vector or matrix of the posterior mean (location parameter), dim = |
Ct |
and |
Rt |
and |
nt |
and |
S |
the vector of the point estimate for the observation variance |
ft |
the vector of the one-step forecast location parameter with length |
Qt |
the vector of the one-step forecast scale parameter with length |
ets |
the vector of the standardised forecast residuals with length |
lpl |
the vector of the Log Predictive Likelihood with length |
West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York.
data("utestdata")
Yt = myts[,1]
Ft = t(cbind(1,myts[,2:5]))
m = dlm.lpl(Yt, Ft, 0.7)