Estimates dynamic communities in multivariate time series (e.g., panel data, longitudinal data, intensive longitudinal data) at multiple time scales and at different levels of analysis: individuals (intraindividual structure), groups, and population (interindividual structure)
dynEGA(
data,
id = NULL,
group = NULL,
n.embed = 5,
tau = 1,
delta = 1,
use.derivatives = 1,
level = c("individual", "group", "population"),
corr = c("auto", "cor_auto", "pearson", "spearman"),
na.data = c("pairwise", "listwise"),
model = c("BGGM", "glasso", "TMFG"),
algorithm = c("leiden", "louvain", "walktrap"),
uni.method = c("expand", "LE", "louvain"),
ncores,
verbose = TRUE,
...
)A list containing:
A list containing:
Estimates --- A list the length of the unique IDs containing
data frames of zero- to second-order derivatives for each ID in data
EstimatesDF --- A data frame of derivatives across all IDs containing
columns of the zero- to second-order derivatives as well as id and
group variables (group is automatically set to 1
for all if no group is provided)
A list containing:
population --- If level includes "populaton", then
the EGA results for the entire sample
group --- If level includes "group", then
a list containing the EGA results for each group
individual --- If level includes "individual", then
a list containing the EGA results for each id
Matrix or data frame. Participants and variable should be in long format such that row t represents observations for all variables at time point t for a participant. The next row, t + 1, represents the next measurement occasion for that same participant. The next participant's data should immediately follow, in the same pattern, after the previous participant
data should have an ID variable labeled "ID"; otherwise, it is
assumed that the data represent the population
For groups, data should have a Group variable labeled "Group";
otherwise, it is assumed that there are no groups in data
Arguments id and group can be specified to tell the function
which column in data it should use as the ID and Group variable, respectively
A measurement occasion variable is not necessary and should be removed from the data before proceeding with the analysis
Numeric or character (length = 1).
Number or name of the column identifying each individual.
Defaults to NULL
Numeric or character (length = 1).
Number of the column identifying group membership.
Defaults to NULL
Numeric (length = 1).
Defaults to 5.
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
Numeric (length = 1).
Defaults to 1.
Number of observations to offset successive embeddings in
the Embed function.
Generally recommended to leave "as is"
Numeric (length = 1).
Defaults to 1.
The time between successive observations in the time series (i.e, lag).
Generally recommended to leave "as is"
Numeric (length = 1).
Defaults to 1.
The order of the derivative to be used in the analysis.
Available options:
0 --- No derivatives; consistent with moving average
1 --- First-order derivatives; interpreted as "velocity" or
rate of change over time
2 --- Second-order derivatives; interpreted as "acceleration" or
rate of the rate of change over time
Generally recommended to leave "as is"
Character vector (up to length of 3). A character vector indicating which level(s) to estimate:
"individual" --- Estimates EGA for each individual in data
(intraindividual structure; requires an "ID" column, see data)
"group" --- Estimates EGA for each group in data
(group structure; requires a "Group" column, see data)
"population" --- Estimates EGA across all data
(interindividual structure)
Character (length = 1).
Method to compute correlations.
Defaults to "auto".
Available options:
"auto" --- Automatically computes appropriate correlations for
the data using Pearson's for continuous, polychoric for ordinal,
tetrachoric for binary, and polyserial/biserial for ordinal/binary with
continuous. To change the number of categories that are considered
ordinal, use ordinal.categories
(see polychoric.matrix for more details)
"cor_auto" --- Uses cor_auto to compute correlations.
Arguments can be passed along to the function
"pearson" --- Pearson's correlation is computed for all
variables regardless of categories
"spearman" --- Spearman's rank-order correlation is computed
for all variables regardless of categories
For other similarity measures, compute them first and input them
into data with the sample size (n)
Character (length = 1).
How should missing data be handled?
Defaults to "pairwise".
Available options:
"pairwise" --- Computes correlation for all available cases between
two variables
"listwise" --- Computes correlation for all complete cases in the dataset
Character (length = 1).
Defaults to "glasso".
Available options:
"BGGM" --- Computes the Bayesian Gaussian Graphical Model.
Set argument ordinal.categories to determine
levels allowed for a variable to be considered ordinal.
See ?BGGM::estimate for more details
"glasso" --- Computes the GLASSO with EBIC model selection.
See EBICglasso.qgraph for more details
"TMFG" --- Computes the TMFG method.
See TMFG for more details
Character or
igraph cluster_* function (length = 1).
Defaults to "walktrap".
Three options are listed below but all are available
(see community.detection for other options):
"leiden" --- See cluster_leiden for more details
"louvain" --- By default, "louvain" will implement the Louvain algorithm using
the consensus clustering method (see community.consensus
for more information). This function will implement
consensus.method = "most_common" and consensus.iter = 1000
unless specified otherwise
"walktrap" --- See cluster_walktrap for more details
Character (length = 1).
What unidimensionality method should be used?
Defaults to "louvain".
Available options:
"expand" --- Expands the correlation matrix with four variables correlated 0.50.
If number of dimension returns 2 or less in check, then the data
are unidimensional; otherwise, regular EGA with no matrix
expansion is used. This method was used in the Golino et al.'s (2020)
Psychological Methods simulation
"LE" --- Applies the Leading Eigenvector algorithm
(cluster_leading_eigen)
on the empirical correlation matrix. If the number of dimensions is 1,
then the Leading Eigenvector solution is used; otherwise, regular EGA
is used. This method was used in the Christensen et al.'s (2023)
Behavior Research Methods simulation
"louvain" --- Applies the Louvain algorithm (cluster_louvain)
on the empirical correlation matrix. If the number of dimensions is 1,
then the Louvain solution is used; otherwise, regular EGA is used.
This method was validated Christensen's (2022) PsyArXiv simulation.
Consensus clustering can be used by specifying either
"consensus.method" or "consensus.iter"
Numeric (length = 1).
Number of cores to use in computing results.
Defaults to ceiling(parallel::detectCores() / 2) or half of your
computer's processing power.
Set to 1 to not use parallel computing
If you're unsure how many cores your computer has,
then type: parallel::detectCores()
Boolean (length = 1).
Should progress be displayed?
Defaults to TRUE.
Set to FALSE to not display progress
Additional arguments to be passed on to
auto.correlate,
network.estimation,
community.detection,
community.consensus, and
EGA
Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
Derivatives for each variable's time series for each participant are
estimated using generalized local linear approximation (see glla).
EGA is then applied to these derivatives to model how variables
are changing together over time. Variables that change together over time are detected
as communities
Generalized local linear approximation
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.
Original dynamic EGA implementation
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.
Time delay embedding procedure
Savitzky, A., & Golay, M. J. (1964).
Smoothing and differentiation of data by simplified least squares procedures.
Analytical Chemistry, 36(8), 1627-1639.
plot.EGAnet for plot usage in EGAnet
# Population structure
simulated_population <- dynEGA(
data = sim.dynEGA, level = "population"
# uses simulated data in package
# useful to understand how data should be structured
)
# Group structure
simulated_group <- dynEGA(
data = sim.dynEGA, level = "group"
# uses simulated data in package
# useful to understand how data should be structured
)
if (FALSE) {
# Individual structure
simulated_individual <- dynEGA(
data = sim.dynEGA, level = "individual",
ncores = 2, # use more for quicker results
verbose = TRUE # progress bar
)
# Population, group, and individual structure
simulated_all <- dynEGA(
data = sim.dynEGA,
level = c("individual", "group", "population"),
ncores = 2, # use more for quicker results
verbose = TRUE # progress bar
)
# Plot population
plot(simulated_all$dynEGA$population)
# Plot groups
plot(simulated_all$dynEGA$group)
# Plot individual
plot(simulated_all$dynEGA$individual, id = 1)
# Step through all plots
# Unless `id` is specified, 4 random IDs
# will be drawn from individuals
plot(simulated_all)}
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