| Title: | Regularized Estimation of Dynamic Linear Regression in the Presence of Autocorrelated Residuals (DREGAR) |
|---|---|
| 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. |
| Authors: | Hamed Haselimashhadi (www.hamedhaseli.webs.com) |
| Maintainer: | Hamed Haselimashhadi <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.3.0 |
| Built: | 2024-11-01 11:16:02 UTC |
| Source: | https://github.com/cran/DREGAR |
Estimating coefficients for penalized/non-penalized dynamic regression in the presence of autocorrelated residuals using an iterative 2-step procedure.
dregar2(data, da = 0, ar = 0, mselection = 4, normalize = FALSE, penalized = TRUE, iteration = 15)dregar2(data, da = 0, ar = 0, mselection = 4, normalize = FALSE, penalized = TRUE, iteration = 15)
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 |
Hamed Haselimashhadi <[email protected]>
dregar6 ,
generateAR ,
sim.dregar
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')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 coefficients for penalized dynamic regression in the presence of autocorrelated residuals using an iterative 6-step procedure.
dregar6(data , da, ar, mselection = 4, type = "alasso", normalize = FALSE, iteration = 15, intercept=FALSE)dregar6(data , da, ar, mselection = 4, type = "alasso", normalize = FALSE, iteration = 15, intercept=FALSE)
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. |
Hamed Haselimashhadi <[email protected]>
dregar2,
generateAR ,
sim.dregar
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')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
generateAR(n = 1, l = -1, u = 1 , min.distance = .Machine$double.eps , sort.coeff = FALSE)generateAR(n = 1, l = -1, u = 1 , min.distance = .Machine$double.eps , sort.coeff = FALSE)
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 |
Hamed Haselimashhadi <[email protected]>
dregar2 ,
dregar6 ,
sim.dregar
generateAR(3 , -1 , 1 , .01 )generateAR(3 , -1 , 1 , .01 )
Simulating a mean zero Gaussian lagged response regression in the presence of autocorrelated residuals
sim.dregar(n = 500 , beta = 1, ind = FALSE , phi = .3, theta = .5, var = 1 , n.z.coeffs=0, shuffle = FALSE , plot = FALSE )sim.dregar(n = 500 , beta = 1, ind = FALSE , phi = .3, theta = .5, var = 1 , n.z.coeffs=0, shuffle = FALSE , plot = FALSE )
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 |
Hamed Haselimashhadi <[email protected]>
dregar2 ,
dregar6 ,
generateAR
simdata=sim.dregar(n = 100 , beta = 1, ind = TRUE , phi = .40 , theta = -.25, var = 1 , plot = TRUE) str(simdata)simdata=sim.dregar(n = 100 , beta = 1, ind = TRUE , phi = .40 , theta = -.25, var = 1 , plot = TRUE) str(simdata)