GIMMEgVAR {GIMMEgVAR}

GIMMEgVAR ' This function calls all the functions needed to fit 'GIMMEgVAR'.

Description

GIMMEgVAR ' This function calls all the functions needed to fit 'GIMMEgVAR'.

Usage

GIMMEgVAR(
  outputPath,
  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"),
  gimmeGVARThreshold = 0.5,
  labelNames = variableNames
)

Arguments

outputPath

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

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). Note: In 'GIMMEgVAR' this matrix is determined and set for the user based on their gimmeThreshold and the results of fitting individual 'graphicalVAR' across all individuals. The logical matrix used to fit determine which elements of the beta matrix are regularized is returned in the gimmegvarFiles folder and is named logicalKappa.

regularize_mat_kappa

A logical matrix indicating which elements of the kappa matrix should be regularized (experimental). Note: In 'GIMMEgVAR' this matrix is determined and set for the user based on their gimmeThreshold and the results of fitting individual 'graphicalVAR' across all individuals. The logical matrix used to fit determine which elements of the beta matrix are regularized is returned in the gvarFiles folder and is named logicalKappa.

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 in the network model

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 unpenalized contemporaneous matrix. Set to "penalized" to mimic version 2.5 and later of sparseTSCGM.

gimmeGVARThreshold

The cutoff value for group-level paths. Defaults to .50, indicating that a path must be non-zero across >= .50 included as a group-level path.

labelNames

Vector of names used to label nodes in Network graph. Defaults to variable names if no vector is supplied.

Value

Returns a list containing 7 elements. The first 6 elements are the group level results indicating the count, proportion and presence (coded 1 for present, 0 for absent) of the edges estimated by the algorithm in the kappa and beta matrices. These results are stored in matrix objects named pathCountBeta, pathProportionBeta, groupBeta and pathCountKappa, pathProportionKappa, and groupKappa respectively and are used to construct the final group level network. The remaining element is a list which contains the person-specific network results for every subject in the data. This is estimated using information from the group-level network model. Along with the the data used to estimate the networks, it contains the following for each subject:

PCC

The partial contemporaneous correlation network

PDC

The partial directed correlation network

beta

The estimated beta matrix

kappa

The estimated kappa matrix

EBIC

The optimal EBIC

path

Results of all tested tuning parameters

labels

A vector containing the node labels

data

A "tsData" object detailing the features of the data used for estimating the network