Type: Package
Title: Regularized Estimation of Dynamic Linear Regression in the Presence of Autocorrelated Residuals (DREGAR)
Version: 0.1.4.0
Date: 2025-05-17
Depends: R(≥ 2.10.0)
Imports: msgps
Maintainer: Hamed Haselimashhadi <hamedhaseli@gmail.com>
Description: A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyLoad: no
Repository: CRAN
NeedsCompilation: no
Packaged: 2025-05-17 11:25:57 UTC; hamed
Date/Publication: 2025-05-17 11:40:02 UTC
Author: Hamed Haselimashhadi [aut, cre]

Estimating (just) adaptive-DREGAR coefficients using an iterative 2-step procedure

Description

Estimating coefficients for penalized/non-penalized dynamic regression in the presence of autocorrelated residuals using an iterative 2-step procedure.

Usage

dregar2(data, da = 0, ar = 0, mselection = 4, 
       normalize = FALSE, penalized = TRUE, 
       iteration = 15)

Arguments

data

Data matrix of order (time, response, covariates)

da

A vector of lags. Autoregressive orders for 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)

normalize

Logical flag. Setting to TRUE to normalize data prior to analysis

penalized

Logical flag. Setting to TRUE to estimate coefficients through penalized likelihood. Otherwise the algorithm applies iterative OLS.

iteration

The number of iterations

Author(s)

Hamed Haselimashhadi <hamedhaseli@gmail.com>

See Also

dregar6 , generateAR , sim.dregar

Examples

par(mfrow=c(2,2))
  m=sim.dregar(n=500 ,  beta=1:4, phi=generateAR(2), theta=.3, 
               n.z.coeffs=3 , plot=TRUE) # generating data
  r=dregar2(data = m$rawdata,da = 1:3,ar = 1:2,mselection = 4,
            penalized = 1 )# estimating parameters using2-step adaptive-DREGAR
  round(cbind(
    true      = c(phi=c(m$phi,0),theta=c(m$theta,0),beta=m$beta),
    estimates = c(phi=r$phi,theta=r$theta,beta=r$beta)
  )
  ,3
  )
  plot(r$obj)
  acf(r$res, main='Residual ACF')
  pacf(r$res,main='Residual PACF')

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')

Generating stationary autoregressive coefficients

Description

Generating stationary autoregressive coefficients

Usage

  generateAR(n = 1, l = -1, u = 1    , 
             min.distance = .Machine$double.eps , 
             sort.coeff = FALSE)

Arguments

n

The number of coefficients

l

Lower bound for coefficients

u

Upper bound for coefficients

min.distance

Minimum distance amongst all pair of coefficients

sort.coeff

Logical flag. If TRUE, then resulting coefficients are sorted descending

Author(s)

Hamed Haselimashhadi <hamedhaseli@gmail.com>

See Also

dregar2 , dregar6 , sim.dregar

Examples

  generateAR(3 , -1 , 1 , .01 )

Simulating data from DREGAR model

Description

Simulating a mean zero Gaussian lagged response regression in the presence of autocorrelated residuals

Usage

  sim.dregar(n = 500    , beta = 1, ind = FALSE  , 
             phi = .3, theta = .5, var = 1 , n.z.coeffs=0,
             shuffle = FALSE     , plot = FALSE  )

Arguments

n

The number of data points to be simulated

beta

Regression coefficients

ind

Logical flag. If TRUE then observations are assumed to be independent. Otherwise they are generated from random AR(1) processes. In both cases, variables are assumed to be mutually independent and follow Gaussian distribution.

phi

Dynamic coefficient(s)

theta

Residuals coefficient(s)

var

Variance of the error term

n.z.coeffs

Number of zero coefficients if needed

shuffle

Logical flag. If TRUE shuffle coefficients. Otherwise data are grouped corresponded to non-zero and zero coefficients.

plot

Logical flag. Plot response

Author(s)

Hamed Haselimashhadi <hamedhaseli@gmail.com>

See Also

dregar2 , dregar6 , generateAR

Examples

  simdata=sim.dregar(n = 100 , beta = 1,
    ind = TRUE , phi = .40 , theta = -.25,
    var = 1 , plot = TRUE)
  str(simdata)