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Apply a Network Estimation Method

Source: R/network.estimation.R
network.estimation.Rd

General function to apply network estimation methods in EGAnet

Usage

network.estimation(
  data,
  n = NULL,
  corr = c("auto", "cor_auto", "cosine", "pearson", "spearman"),
  na.data = c("pairwise", "listwise"),
  model = c("BGGM", "glasso", "TMFG"),
  network.only = TRUE,
  verbose = FALSE,
  ...
)

Arguments

data

Matrix or data frame. Should consist only of variables to be used in the analysis

n

Numeric (length = 1). Sample size if data provided is a correlation matrix

corr

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

  • "cosine" — Uses cosine to compute cosine similarity

  • "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)

na.data

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

model

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

network.only

Boolean (length = 1). Whether the network only should be output. Defaults to TRUE. Set to FALSE to obtain all output for the network estimation method

verbose

Boolean (length = 1). Whether messages and (insignificant) warnings should be output. Defaults to FALSE (silent calls). Set to TRUE to see all messages and warnings for every function call

...

Additional arguments to be passed on to auto.correlate and the different network estimation methods (see model for model specific details)

Value

Returns a matrix populated with a network from the input data

References

Graphical Least Absolute Shrinkage and Selection Operator (GLASSO)
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432–441.

GLASSO with Extended Bayesian Information Criterion (EBICglasso)
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634.

Bayesian Gaussian Graphical Model (BGGM)
Williams, D. R. (2021). Bayesian estimation for Gaussian graphical models: Structure learning, predictability, and network comparisons. Multivariate Behavioral Research, 56(2), 336–352.

Triangulated Maximally Filtered Graph (TMFG)
Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulated maximally filtered graph. Journal of Complex Networks, 5, 161-178.

Author

Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>

Examples

# Load data
wmt <- wmt2[,7:24]

# EBICglasso (default for EGA functions)
glasso_network <- network.estimation(
  data = wmt, model = "glasso"
)

# TMFG
tmfg_network <- network.estimation(
  data = wmt, model = "TMFG"
)