fitGIMMEgVAR {GIMMEgVAR}

FIT 'GIMMEgVAR'

Description

This function fits 'GIMMEgVAR' using the original input data supplied by the user and the logical matrices that result across people after fitting individual 'graphicalVAR'. The logical matrices are determined via the gimmeThreshold specified by the user

Usage

fitGIMMEgVAR(
  inData,
  variableNames,
  gimmeGVARThreshold = 0.5,
  nLambda = 50,
  verbose = TRUE,
  gamma = 0.5,
  scale = TRUE,
  lambda_beta,
  lambda_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", "0.1.2", "0.1.4", "0.1.5", "0.2"),
  beepvar,
  dayvar,
  idvar,
  lags = 1,
  centerWithin = TRUE,
  likelihood = c("unpenalized", "penalized"),
  logicals,
  RES_matrixData,
  outputPath,
  labelNames = variableNames
)

Arguments

inData

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

variableNames

The vector containing name of variables to be analyzed

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.

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.

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'

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

logicals

Logical matrices that determine which elements of the beta and kappa matrices will be regularized when fititng 'GIMMEgVAR'. The matrices are determined in the algorithm by obtaining counts across all fitted individual network results for beta and kappa, respectively. Elements with proportions >= gimmeGVARThreshold value will not be regularized when fitting 'GIMMEgVAR'.

RES_matrixData

List containing results of fitting individual 'graphicalVAR' to each individual.

outputPath

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

labelNames

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

Details

The user can pass in additional 'graphicalVAR' options as specified in the R package 'graphicalVAR' created by Sacha Epskamp. See 'graphicalVAR' documentation for details.

The following results are returned in the 'GIMMEgVAR' Files directory: (1) Data frames containing individual paths. These are the person-specific results for each individual obtained by not regularizing the group paths indicated by the logical matrices for both beta and kappa. They are prefaced RESULTS_GIMMEgVAR_SUBJECT_.

(2) Data frames that indicate the proportion of paths across individuals: These are dataframes containing the proportion of paths present across individuals after fitting 'GIMMEgVAR'. These proportions are used to determine the final group model. They are named proportionKappa.RData and proportionBeta.RData.

(3) Group path data frames for beta and kappa: These are dataframes with 1 indicating the presence of a group path and 0 indicating the absence of a group path. These data frames are derived from #2 above for both beta and kappa. They are the dataframes used to graph the final group models and are prefaced GIMMEgVAR_RESULTS_GRP_BETA_THRESHOLD_ and GIMMEgVAR_RESULTS_GRP_KAPPA_THRESHOLD_. The number following threshold indicates the gimmeThreshold used for the analysis.

(4) Final network graphs are added to the gimmegvarFiles folder in .png format. These contain overlay graphs of the group and individual paths for the beta and kappa network respectively.