BayesianVectorAutoRegression/orderedLasso Package - Examples.R at master · RobertMcPherson/BayesianVectorAutoRegression · GitHub

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rm(list=ls())

#library(knitr)
#purl("TimeSeriesAnalysisWithOrdinalLASSO.rmd")

library(orderedLasso)

##timeLagLasso
#generate simulated data
set.seed(3)
n = 50
maxlag = 5
num_rows_needed = n + maxlag + 1
sigma = 4
x = matrix(rnorm(num_rows_needed * 4), nrow = num_rows_needed)
#create matrix of all possible lags for each variable
x_new = time_lag_matrix(x, maxlag)
b = c(3,1,1,0,0,
      4,1,0,0,0,
      3,2,1,0,0,
      1,0,0,0,0)
y = x_new %*% b + sigma* rnorm(nrow(x_new))
y = as.vector(y)
y = c(y, rnorm(maxlag + 1))
model1 = timeLagLasso(x = x, y = y, lambda = 1, maxlag = maxlag,
                      method = "Solve.QP", strongly.ordered = TRUE)


##timeLagLasso.cv
#generate simulated data
set.seed(3)
n = 50
maxlag = 5
num_rows_needed = n + maxlag + 1
sigma = 4
x = matrix(rnorm(num_rows_needed * 4), nrow = num_rows_needed)
#create matrix of all possible lags for each variable
x_new = time_lag_matrix(x, maxlag)
b = c(3,1,1,0,0,
      4,1,0,0,0,
      3,2,1,0,0,
      1,0,0,0,0)
y = x_new %*% b + sigma* rnorm(nrow(x_new))
y = as.vector(y)
y = c(y, rnorm(maxlag + 1))
cvmodel = timeLagLasso.cv(x= x, y = y, maxlag = 5, method = "Solve.QP")

##timeLagLasso.path
##Fit a path of time-lasso models
#generate simulated data
set.seed(3)
n = 50
maxlag = 5
num_rows_needed = n + maxlag + 1
sigma = 4
x = matrix(rnorm(num_rows_needed * 4), nrow = num_rows_needed)
#create matrix of all possible lags for each variable
x_new = time_lag_matrix(x, maxlag)
b = c(3,1,1,0,0,
      4,1,0,0,0,
      3,2,1,0,0,
      1,0,0,0,0)
y = x_new %*% b + sigma* rnorm(nrow(x_new))
y = as.vector(y)
y = c(y, rnorm(maxlag + 1))
#time lag lasso path discovery
path1 = timeLagLasso.path(x= x, y = y, maxlag = 5, method = "Solve.QP", strongly.ordered = TRUE)
plot(path1)