dynEGA.ind.pop {EGAnet}R Documentation

Dynamic EGA used in the mctest.ergoInfo function


Dynamic EGA used in the mctest.ergoInfo function. DynEGA estimates dynamic factors in multivariate time series (i.e. longitudinal data, panel data, intensive longitudinal data) at multiple time scales, in different levels of analysis: individuals (intraindividual structure) and population (structure of the population). Exploratory graph analysis is applied in the derivatives estimated using generalized local linear approximation (glla). Instead of estimating factors by modeling how variables are covarying, as in traditional EGA, dynEGA is a dynamic model that estimates the factor structure by modeling how variables are changing together. GLLA is a filtering method for estimating derivatives from data that uses time delay embedding and a variant of Savitzky-Golay filtering to accomplish the task.


  tau = 1,
  delta = 1,
  id = NULL,
  use.derivatives = 1,
  model = c("glasso", "TMFG"),
  model.args = list(),
  algorithm = c("walktrap", "louvain"),
  algorithm.args = list(),
  corr = c("cor_auto", "pearson", "spearman"),



A dataframe with the variables to be used in the analysis. The dataframe should be in a long format (i.e. observations for the same individual (for example, individual 1) are placed in order, from time 1 to time t, followed by the observations from individual 2, also ordered from time 1 to time t.)


Integer. Number of embedded dimensions (the number of observations to be used in the Embed function). For example, an "n.embed = 5" will use five consecutive observations to estimate a single derivative.


Integer. Number of observations to offset successive embeddings in the Embed function. A tau of one uses adjacent observations. Default is "tau = 1".


Integer. The time between successive observations in the time series. Default is "delta = 1".


Numeric. Number of the column identifying each individual.


Integer. The order of the derivative to be used in the EGA procedure. Default to 1.


Character. A string indicating the method to use. Defaults to glasso. Current options are:

  • glasso Estimates the Gaussian graphical model using graphical LASSO with extended Bayesian information criterion to select optimal regularization parameter. This is the default method

  • TMFG Estimates a Triangulated Maximally Filtered Graph


List. A list of additional arguments for EBICglasso.qgraph or TMFG


A string indicating the algorithm to use or a function from igraph

Current options are:


List. A list of additional arguments for cluster_walktrap, cluster_louvain, or some other community detection algorithm function (see examples)


Type of correlation matrix to compute. The default uses cor_auto. Current options are:

  • cor_auto Computes the correlation matrix using the cor_auto function from qgraph.

  • pearson Computes Pearson's correlation coefficient using the pairwise complete observations via the cor function.

  • spearman Computes Spearman's correlation coefficient using the pairwise complete observations via the cor function.


Numeric. Number of cores to use in computing results. Defaults to parallel::detectCores() / 2 or half of your computer's processing power. Set to 1 to not use parallel computing. Recommended to use maximum number of cores minus one

If you're unsure how many cores your computer has, then use the following code: parallel::detectCores()


Additional arguments. Used for deprecated arguments from previous versions of EGA


Hudson Golino <hfg9s at virginia.edu>


## Not run: 
\donttest{# Population structure:
dyn.ega1 <- dynEGA.ind.pop(data = sim.dynEGA, n.embed = 5, tau = 1,
delta = 1, id = 21, use.derivatives = 1, model = "glasso", ncores = 2,
cor = "pearson")

## End(Not run)

[Package EGAnet version 1.1.0 Index]