ntwk (version 1.0.0)

grou_regularisation: Regularisation schemes for the GrOU process that implements a Lasso, Ridge or Adaptive Lasso penalty.

Description

Regularisation schemes for the GrOU process that implements a Lasso, Ridge or Adaptive Lasso penalty.

Usage

grou_regularisation(
  times,
  data,
  thresholds = NA,
  lambda = NA,
  reg = "l1",
  div = 1e+05,
  output = "vector",
  gamma = NA,
  cut_off = NA,
  use_scaling = FALSE
)

Arguments

times

Times at which data is given

data

Values to compute the MLE with.

thresholds

Jump threshold values.

lambda

Penalty parameter.

reg

Type of penalty (l1, l2 or adaptive).

div

Batch size/divisor to avoid large memory allocation.

output

Output type: either "vector"or "matrix".

gamma

Adaptive MLE scaling parameter.

cut_off

Sparsity proportion, defaults to NA.

use_scaling

Brownian motion covariance matrix scaling in the likelihood.

Value

Regularised dynamics matrix.

Examples

# NOT RUN {
n <- 1000
d <- 10
times <- seq(n)
delta_time <- 0.01
noise <- matrix(rnorm(n * d, sd = sqrt(delta_time)), ncol = d)
data <- construct_path(
  diag(d),
  noise = noise, y_init = rep(0, d), delta_time = delta_time
)
# }
# NOT RUN {
    grou_regularisation(times = times, data = data, lambda = 1, div = 1e2)
# }