grid.DMA: Computes 'fDMA' Function for Multiple Values of 'alpha' and...

In fDMA: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes

Computes fDMA Function for Multiple Values of alpha and lambda.

Description

Sometimes it is necessary to consider various values of parameters alpha and lambda in Dynamic Model Averaging (or Dynamic Model Selection, etc.). This function computes fDMA function for all combinations of alpha and lambda for given grids.

This function is a wrapper of fDMA.

Usage

grid.DMA(y,x,grid.alpha,grid.lambda,initvar,W=NULL,initial.period=NULL,V.meth=NULL,
kappa=NULL,gprob=NULL,omega=NULL,model=NULL,parallel.grid=NULL,m.prior=NULL,
mods.incl=NULL,DOW=NULL,DOW.nmods=NULL,DOW.type=NULL,DOW.limit.nmods=NULL,
forced.models=NULL,forbidden.models=NULL,forced.variables=NULL,bm=NULL,
small.c=NULL,av=NULL)

Arguments

y	see fDMA
x	see fDMA
grid.alpha	a numeric vector of different values of alpha
grid.lambda	a numeric vector of different values of lambda or a list of numeric vectors for multiple lambda in one model (see fDMA)
initvar	see fDMA
W	see fDMA
initial.period	see fDMA
V.meth	see fDMA
kappa	see fDMA
gprob	see fDMA
omega	see fDMA
model	see fDMA
parallel.grid	optional, logical, indicate whether parallel computations should be used, by default parallel.grid=FALSE
m.prior	see fDMA
mods.incl	see fDMA
DOW	see fDMA
DOW.nmods	see fDMA
DOW.type	see fDMA
DOW.limit.nmods	see fDMA
forced.models	see fDMA
forbidden.models	see fDMA
forced.variables	see fDMA
bm	see fDMA
small.c	see fDMA
av	see fDMA

Value

an object of class grid.dma, list of

$models	list of list of models
$RMSE	matrix with Root Mean Squared Error (RMSE) for all estimated models
$MAE	matrix with Mean Absolute Error (MAE) for all estimated models

See Also

print.grid.dma, summary.grid.dma, plot.grid.dma.

Examples

wti <- crudeoil[-1,1]
drivers <- (lag(crudeoil[,-1],k=1))[-1,]
ld.wti <- (diff(log(wti)))[-1,]
ld.drivers <- drivers[-1,]
ld.drivers[,c(4,6)] <- (diff(drivers[,c(4,6)]))[-1,]
ld.drivers[,c(1:2,5,7)] <- (diff(log(drivers[,c(1:2,5,7)])))[-1,]
ld.drivers[,c(3,6)] <- ld.drivers[,c(3,6)]/100

gra <- c(0.99,0.98,0.97)
grl <- c(0.99,0.95)
g1 <- grid.DMA(y=ld.wti,x=ld.drivers,grid.alpha=gra,grid.lambda=grl,initvar=10)


# extract model with alpha=0.97 and lambda=0.95
model <- g$models[[3]][[2]]

# models with various multiple lambdas

gra <- c(0.99,0.98,0.97)
grl <- list(c(0.99,0.95,0.90),c(0.99,0.98,0.97,0.96,0.95))
g2 <- grid.DMA(y=ld.wti,x=ld.drivers,grid.alpha=gra,grid.lambda=grl,initvar=10)

fDMA documentation built on April 4, 2025, 5:16 a.m.