Computes a Monte-Carlo Test for the Ergodicity Information Index, comparing the
empirical Ergodicity Information index to values obtained in a Monte-Carlo simulation in which all individuals
have a similar latent structure. The p-values in the Monte-Carlo test can be calculated as (sum(EII>=MC.EII)+1)/(iter+1) and as
(sum(EII<=MC.EII)+1)/(iter+1), where EII is the empirical Ergodicity Information Index, MC.EII is the values of the Ergodicity Information Index obtained
in the simulation, 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 Monte-Carlo Test for the Ergodicity Information Index,
the null hypothesis is that the empirical value of EII is equal to the Monte-Carlo value of EII obtained in multiple individuals with a similar latent structure.
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, 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, conditioned on the parameters used to simulate the data.
mctest.ergoInfo(
iter,
N,
EII,
use,
variab,
timep,
nfact,
error,
dfm,
loadings,
autoreg,
crossreg,
var.shock,
cov.shock,
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
Numeric integer.
Number of random samples to generate in the Monte-Carlo simulation.
At least 500 is recommended
Numeric integer.
Number of individuals to simulate data from, using the simDFM function.
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.
Number of variables per factor.
Number of time points.
Number of factors.
Value to be used to construct a diagonal matrix Q. This matrix is p x p covariance matrix Q that will generate random errors following a multivariate normal distribution with mean zeros. The value provided is squared before constructing Q.
A string indicating the dynamical factor model to use. Defaults to "DAFS".
Current options are:
DAFS
Simulates data using the direct autoregressive factor score model.
This is the default method
RandomWalk
Simulates data using a dynamic factor model with random walk factor scores.
Magnitude of the loadings.
Magnitude of the autoregression coefficients.
Default is "autoreg = 0.8".
Magnitude of the cross-regression coefficients.
Default is "crossreg = 0.1".
Magnitude of the random shock variance.
Default is "var.shock = 0.18".
Magnitude of the random shock covariance
Default is "cov.shock = 0.36".
Integer.
Number of embedded dimensions (the number of observations to be used in the Embed function). For example,
an "embed = 5" will use five observations to estimate a single derivative. Defaults to 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>