fitGraphicalVAR {GIMMEgVAR}

fitgraphicalVAR

Description

Fits 'graphicalVAR' with defaults used in 'graphicalVAR' package developed by Sacha Epskamp. See 'graphicalVAR' documentation by Sacha Epskamp for details.

Usage

fitGraphicalVAR(
  data,
  nLambda = 50,
  verbose = TRUE,
  gamma,
  scale = TRUE,
  lambda_beta,
  lambda_kappa,
  regularize_mat_beta,
  regularize_mat_kappa,
  maxit.in = 100,
  maxit.out = 100,
  deleteMissings = TRUE,
  penalize.diagonal = TRUE,
  lambda_min_kappa = 0.05,
  lambda_min_beta = lambda_min_kappa,
  mimic = c("current"),
  variableNames,
  beepvar,
  dayvar,
  idvar,
  lags = 1,
  centerWithin = TRUE,
  likelihood = c("unpenalized", "penalized"),
  outputPath
)

Arguments

data

A matrix or data frame containing repeated measures (rows) on a set of variables (columns). Must not contain missing data.

nLambda

The number of both lambda parameters to test. Defaults to 50, which results in 2500 models to evaluate.

verbose

Logical, should a progress bar be printed to the console?

gamma

The EBIC hyper-parameter. Set to 0 to use regular BIC.

scale

Logical, should responses be standardized before estimation?

lambda_beta

An optional vector of lambda_beta values to test. Set lambda_beta = 0 argument and lambda_kappa = 0 for unregularized estimation.

lambda_kappa

An optional vector of lambda_kappa values to test. Set lambda_beta = 0 argument and lambda_kappa = 0 for unregularized estimation.

regularize_mat_beta

A logical matrix indicating which elements of the beta matrix should be regularized (experimental).

regularize_mat_kappa

A logical matrix indicating which elements of the kappa matrix should be regularized (experimental).

maxit.in

Maximum number of iterations in the inner loop (computing beta)

maxit.out

Maximum number of iterations in the outer loop

deleteMissings

Logical, should missing responses be deleted?

penalize.diagonal

Logical, should the diagonal of beta be penalized (i.e., penalize auto-regressions)?

lambda_min_kappa

Multiplier of maximal tuning parameter for kappa

lambda_min_beta

Multiplier of maximal tuning parameter for beta

mimic

Allows one to mimic earlier versions of 'graphicalVAR'

variableNames

The vector containing name of variables to be analyzed

beepvar

String indicating assessment beep per day (if missing, is added). Adding this argument will cause non-consecutive beeps to be treated as missing!

dayvar

String indicating assessment day. Adding this argument makes sure that the first measurement of a day is not regressed on the last measurement of the previous day. IMPORTANT: only add this if the data has multiple observations per day.

idvar

String indicating the subject ID

lags

Vector of lags to include

centerWithin

logical, should subject data be within-person centered before estimating fixed effects?

likelihood

Should likelihood be computed based on penalized contemporaneous matrix or

outputPath

The user specified path to the directory where results files should be stored.

Details

The following results are returned in the gvarFiles directory: (1) Separate data frames containing the usual individual 'graphicalVAR' results. These dataframes contain the person-specific results for each individual obtained by fitting 'graphicalVAR'. They are prefixed RESULTS_GVAR_SUBJECT The number of data frames returned equals the number of individuals whose models were successfully fitted from the input data file.