Computes a parametric Bootstrap Test for the Ergodicity Information Index, comparing the
empirical Ergodicity Information index to values obtained in data generated using N parametric bootstraps of the correlation matrix estimated using the
dynEGA function, for the population structure. The p-values in the bootstrap test can be calculated as (sum(EII>=boot.EII)+1)/(iter+1) and as
(sum(EII<=boot.EII)+1)/(iter+1), where EII is the empirical Ergodicity Information Index, boot.EII is the values of the Ergodicity Information Index obtained
in the bootstraped samples, and iter is the number of random samples generated in the simulation. The two-sided p-value is computed as two times the lowest p-value. In the bootstrap Test for the Ergodicity Information Index,
the null hypothesis is that the empirical value of EII is equal to the values of EII obtained in multiple individuals with the same structure as the population structure estimated
via dynEGA.
Small values of p indicate that is very unlikely to obtain an EII as large as the one obtained in the empirical sample if the null hypothesis is true (i.e. all individuals have the same structure as the population structure), thus there is convincing evidence that the empirical Ergodicity Information Index is
different than it could be expected if all individuals had a similar latent structure.
boot.ergoInfo(
dynEGA.pop,
iter,
EII,
use,
embed,
tau,
delta,
derivatives,
model,
model.args = list(),
algorithm = c("walktrap", "louvain"),
algorithm.args = list(),
corr,
ncores,
...
)Returns a list containing:
The values of the Ergodicity Information Index obtained in the Monte-Carlo Simulation
The p-value of the Monte-Carlo test for the Ergodicity Information Index. The null hypothesis is that the empirical Ergodicity Information index is equal to the expected value of the EII if the all individuals had similar latent structures.
Indicates wheter the empirical EII is greater or less then the Monte-Carlo obtained EII.
Histogram of the bootstrapped ergodicity information index
A dynEGA or a dynEGA.pop.ind object.
Numeric integer.
Number of random samples to generate in the Monte-Carlo simulation.
At least 500 is recommended
Numeric.
Empirical Ergodicity Information Index obtained via the ergoInfo function.
Character.
A string indicating what network element will be used to compute the algorithm complexity in the ergoInfo function,
the list of edges or the weights of the network.
Defaults to use = "edge.list".
Current options are:
edge.list
Calculates the algorithm complexity using the list of edges.
weights
Calculates the algorithm complexity using the weights of the network.
Integer.
Number of embedded dimensions (the number of observations to be used in the Embed function). For example,
an "embed = 5" will use five consecutive observations to estimate a single derivative.
Default is "embed = 5".
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".
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:
walktrap
Computes the Walktrap algorithm using cluster_walktrap
louvain
Computes the Walktrap algorithm using cluster_louvain
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>