mlGraphicalVAR: Pooled and individual graphical VAR estimation

In graphicalVAR: Graphical VAR for Experience Sampling Data

Pooled and individual graphical VAR estimation

Description

This function fits fixed effect temporal and contemporaneous networks over multiple subjects and runs separate graphical VAR models per subject. The algorithm does: (1) pool all data, within-subject center variables and run graphicalVAR to obtain fixed effects, (2) run EBICglasso on subject means to obtain a between-subjects network, (3) run graphicalVAR on data of every subject to obtain individual networks. See arxiv.org/abs/1609.04156 for more details.

Usage

mlGraphicalVAR(data, vars, beepvar, dayvar, idvar, scale = TRUE,
              centerWithin = TRUE, gamma = 0.5, verbose = TRUE,
              subjectNetworks = TRUE, lambda_min_kappa_fixed = 0.001,
              lambda_min_beta_fixed = 0.001, lambda_min_kappa = 0.05,
              lambda_min_beta = lambda_min_kappa, lambda_min_glasso = 0.01,
              ...)

Arguments

data	Data frame
vars	Vectors of variables to include in the analysis
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
scale	Logical, should variables be standardized before estimation?
centerWithin	Logical, should subject data be within-person centered before estimating fixed effects?
gamma	EBIC tuning parameter.
verbose	Logical indicating if console messages and the progress bar should be shown.
subjectNetworks	TRUE to estimate all subject numbers, or a vector with IDs of which subject numbers should be estimated.
lambda_min_kappa_fixed	Multiplier of maximal tuning parameter
lambda_min_beta_fixed	Multiplier of maximal tuning parameter
lambda_min_kappa	Multiplier of maximal tuning parameter
lambda_min_beta	Multiplier of maximal tuning parameter
lambda_min_glasso	Multiplier of maximal tuning parameter
...	Arguments sent to graphicalVAR

Value

A "mlGraphicalVAR" object with the following elements:

fixedPCC	Estimated fixed effects (partial contemporaneous correlations) of contemporaneous effects
fixedPDC	Estimated fixed effects (partial directed correlations) of temporal effects
fixedResults	Full object of pooled data estimation (fixed effects)
betweenNet	Estimated between-subjects network (partial correlations)
ids	Vector of subject IDs
subjectPCC	List of estimated individual contemporaneous networks
subjectPDC	List of estimated individual directed networks
subjecResults	List of full results of individual estimations

Author(s)

Sacha Epskamp <[e-mail]>

References

Epskamp, S., Waldorp, L. J., Mottus, R., & Borsboom, D. Discovering Psychological Dynamics: The Gaussian Graphical Model in Cross-sectional and Time-series Data.

See Also

graphicalVAR

Examples

## Not run: 
# Simulate data:
Sim <- simMLgvar(nTime = 50, nPerson = 20, nVar = 3)

# Estimate model:
Res <- mlGraphicalVAR(Sim$data, vars = Sim$vars, idvar = Sim$idvar)

layout(t(1:2))
library("qgraph")

# Temporal fixed effects
qgraph(Res$fixedPDC, title = "Estimated fixed PDC", layout = "circle")
qgraph(Sim$fixedPDC, title = "Simulated fixed PDC", layout = "circle")

# Contemporaneous fixed effects
qgraph(Res$fixedPCC, title = "Estimated fixed PCC", layout = "circle")
qgraph(Sim$fixedPCC, title = "Simulated fixed PCC", layout = "circle")

## End(Not run)

graphicalVAR documentation built on Oct. 18, 2023, 9:09 a.m.