determine_beta {RCTS}

Helpfunction in estimate_beta() for estimating beta_est.

Description

Helpfunction in estimate_beta() for estimating beta_est.

Usage

determine_beta(
  string,
  X_special,
  Y_special,
  robust,
  NN,
  TT,
  S,
  method_estimate_beta,
  initialisation = FALSE,
  indices = NA,
  vars_est,
  sigma2,
  nosetting_local = FALSE,
  kappa_candidates = c(2^(-0:-20), 0)
)

Arguments

string

can have values: "homogeneous" (when one beta_est is estimated for all individuals together) or "heterogeneous" (when beta_est is estimated either groupwise or elementwise)

X_special

preprocessed X (2-dimensional matrix with 'var_est' observable variables)

Y_special

preprocessed Y

robust

robust or classical estimation

NN

number of time series

TT

length of time series

S

estimated number of groups

method_estimate_beta

defines how beta is estimated. Default case is an estimated beta for each individual. Default value is "individual." Possible values are "homogeneous", "group" or "individual".

initialisation

indicator of being in the initialisation phase

indices

individuals for which beta_est is being estimated

vars_est

number of available observed variables for which a coefficient will be estimated. As default it is equal to the number of available observed variables.

sigma2

sum of squared error terms, scaled by NT

nosetting_local

option to remove the recommended setting in lmrob(). It is much faster. Defaults to FALSE.

kappa_candidates

Defines the size of the SCAD-penalty used in the classical algorithm. This vector should contain more than 1 element.

Value

The function returns a numeric vector (for the default setting: string == "heterogeneous") or a matrix with the estimated beta (if string == "homogeneous").