R: Pooled and individual graphical VAR estimation

mlGraphicalVAR {graphicalVAR} | R Documentation

## 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 <mail@sachaepskamp.com>

### References

Epskamp, S., Waldorp, L. J., M\~ottus, 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)

[Package graphicalVAR version 0.3 Index]