multivar (version 1.1.0)

constructModel: Construct an object of class multivar

Description

Construct an object of class multivar

Usage

constructModel(
  data = NULL,
  lag = 1,
  horizon = 0,
  t1 = NULL,
  t2 = NULL,
  lambda1 = NULL,
  lambda2 = NULL,
  nlambda1 = 30,
  nlambda2 = 30,
  depth = 1000,
  tol = 1e-04,
  window = 1,
  standardize = T,
  weightest = "lasso",
  canonical = FALSE,
  threshold = FALSE,
  lassotype = "adaptive",
  intercept = FALSE,
  W = NULL,
  ratios = NULL,
  cv = "blocked",
  nfolds = 10,
  thresh = 0,
  lamadapt = FALSE
)

Arguments

data

List. A list (length = k) of T by d multivariate time series

lag

Numeric. The VAR order. Default is 1.

horizon

Numeric. Desired forecast horizon. Default is 1. ZF Note: Should probably be zero.

t1

Numeric. Index of time series in which to start cross validation. If NULL, default is floor(nrow(n)/3) where nk is the time series length for individual k.

t2

Numeric. Index of times series in which to end cross validation. If NULL, default is floor(2*nrow(n)/3) where nk is the time series length for individual k.

lambda1

Matrix. Regularization parameter 1. Default is NULL.

lambda2

Matrix. Regularization parameter 2. Default is NULL.

nlambda1

Numeric. Number of lambda1 values to search over. Default is 30.

nlambda2

Numeric. Number of lambda2 values to search over. Default is 30.

depth

Numeric. Depth of grid construction. Default is 1000.

tol

Numeric. Optimization tolerance (default 1e-4).

window

Numeric. Size of rolling window.

standardize

Logical. Default is true. Whether to standardize the individual data.

weightest

Character. Default is "mlr" for multiple linear regression. "sls" for simple linear regression also available. How to estimate the first-stage weights.

canonical

Logical. Default is false. If true, individual datasets are fit to a VAR(1) model.

threshold

Logical. Default is false. If true, and canonical is true, individual transition matrices are thresholded based on significance.

lassotype

Character. Default is "adaptive". Choices are "standard" or "adaptive" lasso.

intercept

Logical. Default is FALSE.

W

Matrix. Default is NULL.

ratios

Numeric vector. Default is NULL.

cv

Character. Default is "rolling" for rolling window cross-validation. "blocked" is also available for blocked folds cross-validation. If "blocked" is selected the nfolds argument should bbe specified.

nfolds

Numeric. The number of folds for use with "blocked" cross-validation.

thresh

Numeric. Post-estimation threshold for setting the individual-level coefficients to zero if their absolute value is smaller than the value provided. Default is zero.

lamadapt

Logical. Should the lambdas be calculated adaptively. Default is FALSE.

Examples

# NOT RUN {
sim  <- multivar_sim(
  k = 2,  # individuals
  d = 3,  # number of variables
  n = 20, # number of timepoints
  prop_fill_com = 0.1, # proportion of paths common
  prop_fill_ind = 0.1, # proportion of paths unique
  lb = 0.1,  # lower bound on coefficient magnitude
  ub = 0.9,  # upper bound on coefficient magnitude
  sigma = diag(3) # noise
)

plot_sim(sim, plot_type = "common")

model <- constructModel(data = sim$data, weightest = "ols")

# }