mctest.ergoInfo {EGAnet}  R Documentation 
Computes a MonteCarlo Test for the Ergodicity Information Index, comparing the
empirical Ergodicity Information index to values obtained in a MonteCarlo simulation in which all individuals
have a similar latent structure. The pvalues in the MonteCarlo 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 twosided pvalue is computed as two times the lowest pvalue. In the MonteCarlo Test for the Ergodicity Information Index,
the null hypothesis is that the empirical value of EII is equal to the MonteCarlo 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,
...
)
iter 
Numeric integer.
Number of random samples to generate in the MonteCarlo simulation.
At least 
N 
Numeric integer.
Number of individuals to simulate data from, using the 
EII 
Numeric.
Empirical Ergodicity Information Index obtained via the 
use 
Character.
A string indicating what network element will be used to compute the algorithm complexity in the

variab 
Number of variables per factor. 
timep 
Number of time points. 
nfact 
Number of factors. 
error 
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. 
dfm 
A string indicating the dynamical factor model to use. Defaults to

loadings 
Magnitude of the loadings. 
autoreg 
Magnitude of the autoregression coefficients.
Default is 
crossreg 
Magnitude of the crossregression coefficients.
Default is 
var.shock 
Magnitude of the random shock variance.
Default is 
cov.shock 
Magnitude of the random shock covariance
Default is 
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 
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. Defaults to

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 
Returns a list containing:
mc.ergoInfo 
The values of the Ergodicity Information Index obtained in the MonteCarlo Simulation 
p.value.twosided 
The pvalue of the MonteCarlo 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. 
effect 
Indicates wheter the empirical EII is greater or less then the MonteCarlo obtained EII. 
plot.dist 
Histogram of the bootstrapped ergodicity information index 
Hudson Golino <hfg9s at virginia.edu>
## Not run:
\donttest{
dyn1 < dynEGA.ind.pop(data = sim.dynEGA, n.embed = 5, tau = 1,
delta = 1, id = 21, group = 22, use.derivatives = 1,
model = "glasso", ncores = 2, corr = "pearson")
eii1 < ergoInfo(data = dyn1)$EII
dist.ergoinfo < mctest.ergoInfo(iter = 10, N = 10, EII = eii1,
variab = 4,
timep = 100, nfact = 2, error = 0.05, dfm = "DAFS", loadings = 0.55, autoreg = 0.8,
crossreg = 0.1, var.shock = 0.18, cov.shock = 0.36, embed = 5, tau=1, delta=1, derivatives=1,
model = "glasso", ncores = 2, corr = "pearson")
}
## End(Not run)