dregar6 {DREGAR} | R Documentation |
Estimating adaptive/non-adaptive DREGAR coefficients using an iterative 6-step procedure
Description
Estimating coefficients for penalized dynamic regression in the presence of autocorrelated residuals using an iterative 6-step procedure.
Usage
dregar6(data , da, ar, mselection = 4, type = "alasso",
normalize = FALSE, iteration = 15, intercept=FALSE)
Arguments
data |
Data matrix of order (time, response, covariates) |
da |
A vector of lags. Autoregressive orders for the response. For example 1:p for all lags from 1 to p |
ar |
A vector of lags. Autoregressive orders for residuals. For example 1:q for all lags from 1 to q |
mselection |
Model selection criteria. Choosing among 1 (CP), 2 (AIC), 3 (GCV) and 4 (BIC) |
type |
Type of penalty. Choosing between 'enet' and 'alasso' for DREGAR and adaptive-DREGAR penalties. |
normalize |
Logical flag. Setting to TRUE to normalise data prior to analysis |
iteration |
The number of iterations |
intercept |
Logical flag. Setting to TRUE to have intercept in the model. |
Author(s)
Hamed Haselimashhadi <hamedhaseli@gmail.com>
See Also
dregar2
,
generateAR
,
sim.dregar
Examples
par(mfrow=c(2,2))
m=sim.dregar(n=500 , beta=1:4, phi=generateAR(2), theta=.1,
n.z.coeffs=3 , plot=TRUE) # generating data
r=dregar6(data=m$rawdata, da = 1:3,
ar = 1:2,mselection = 4,
type='alasso')# estimating parameters using (non-apdative) DREGAR
round(cbind(
true = c(phi=c(m$phi,0),theta=c(m$theta,0),beta=m$beta),
estimates = c(phi=r$phi,theta=r$theta,beta=r$beta)
)
,3
)
plot(r$mod.phi,main='phi')
plot(r$mod.theta,main='theta')
plot(r$mod.beta,main='beta')