dynEGA {EGAnet} | R Documentation |
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), groups or 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.
dynEGA(
data,
n.embed,
tau = 1,
delta = 1,
level = c("individual", "group", "population"),
id = NULL,
group = NULL,
use.derivatives = 1,
model = c("glasso", "TMFG"),
model.args = list(),
algorithm = c("walktrap", "louvain"),
algorithm.args = list(),
corr = c("cor_auto", "pearson", "spearman"),
ncores,
...
)
data |
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.) |
n.embed |
Integer.
Number of embedded dimensions (the number of observations to be used in the |
tau |
Integer.
Number of observations to offset successive embeddings in the |
delta |
Integer.
The time between successive observations in the time series.
Default is |
level |
Character.
A string indicating the level of analysis. If the interest is
in modeling the intraindividual structure only (one dimensionality structure per individual), then Current options are:
|
id |
Numeric. Number of the column identifying each individual. |
group |
Numeric or character.
Number of the column identifying group membership. Must be specified only if |
use.derivatives |
Integer. The order of the derivative to be used in the EGA procedure. Default to 1. |
model |
Character. A string indicating the method to use. Current options are:
|
model.args |
List.
A list of additional arguments for |
algorithm |
A string indicating the algorithm to use or a function from Current options are:
|
algorithm.args |
List.
A list of additional arguments for |
corr |
Type of correlation matrix to compute. The default uses
|
ncores |
Numeric.
Number of cores to use in computing results.
Defaults to If you're unsure how many cores your computer has,
then use the following code: |
... |
Additional arguments.
Used for deprecated arguments from previous versions of |
Hudson Golino <hfg9s at virginia.edu>
Boker, S. M., Deboeck, P. R., Edler, C., & Keel, P. K. (2010) Generalized local linear approximation of derivatives from time series. In S.-M. Chow, E. Ferrer, & F. Hsieh (Eds.), The Notre Dame series on quantitative methodology. Statistical methods for modeling human dynamics: An interdisciplinary dialogue, (p. 161-178). Routledge/Taylor & Francis Group.
Deboeck, P. R., Montpetit, M. A., Bergeman, C. S., & Boker, S. M. (2009) Using derivative estimates to describe intraindividual variability at multiple time scales. Psychological Methods, 14(4), 367-386.
Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2021). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. Psychometrika.
Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639.
# Population structure:
## plot.type = "qqraph" used for CRAN checks
## plot.type = "GGally" is the default
dyn.random <- dynEGA(data = sim.dynEGA, n.embed = 5, tau = 1,
delta = 1, id = 21, group = 22, use.derivatives = 1,
level = "population", model = "glasso", ncores = 2)
plot(dyn.random, plot.type = "qgraph")
# Group structure:
dyn.group <- dynEGA(data = sim.dynEGA, n.embed = 5, tau = 1,
delta = 1, id = 21, group = 22, use.derivatives = 1,
level = "group", model = "glasso", ncores = 2)
plot(dyn.group, ncol = 2, nrow = 1, plot.type = "qgraph")
# Intraindividual structure (commented out for CRAN tests):
# dyn.individual <- dynEGA(data = sim.dynEGA, n.embed = 5, tau = 1,
# delta = 1, id = 21, group = 22, use.derivatives = 1,
# level = "individual", model = "glasso", ncores = 2)