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Exploratory Graph Model

Source: R/EGM.R
EGM.Rd

Function to fit the Exploratory Graph Model

Usage

EGM(
  data,
  EGM.model = c("standard", "EGA"),
  communities = NULL,
  structure = NULL,
  search = FALSE,
  p.in = NULL,
  p.out = NULL,
  opt = c("AIC", "BIC", "CFI", "chisq", "logLik", "RMSEA", "SRMR", "TEFI", "TEFI.adj",
    "TLI"),
  constrained = TRUE,
  verbose = TRUE,
  ...
)

Arguments

data

Matrix or data frame. Should consist only of variables to be used in the analysis. Can be raw data or a correlation matrix

EGM.model

Character vector (length = 1). Sets the procedure to conduct EGM. Available options:

  • "EGA" (default) — Applies EGA to obtain the (sparse) regularized network structure, communities, and memberships

  • "standard" — Applies the standard EGM model which estimates communities based on the non-regularized empirical partial correlation matrix and sparsity is set using p.in and p.out

communities

Numeric vector (length = 1). Number of communities to use for the "standard" type of EGM. Defaults to NULL. Providing no input will use the communities and memberships output from the Walktrap algorithm (cluster_walktrap) based on the empirical non-regularized partial correlation matrix

structure

Numeric or character vector (length = ncol(data)). Can be theoretical factors or the structure detected by EGA. Defaults to NULL

Boolean (length = 1). Whether a search over parameters should be conducted. Defaults to FALSE. Set to TRUE to select a model over a variety of parameters that optimizes the opt objective

p.in

Numeric vector (length = 1). Probability that a node is randomly linked to other nodes in the same community. Within community edges are set to zero based on quantile(x, prob = 1 - p.in) ensuring the lowest edge values are set to zero (i.e., most probable to not be randomly connected). Only used for EGM.type = "standard". Defaults to NULL but must be set

p.out

Numeric vector (length = 1). Probability that a node is randomly linked to other nodes not in the same community. Between community edges are set to zero based on quantile(x, prob = 1 - p.out) ensuring the lowest edge values are set to zero (i.e., most probable to not be randomly connected). Only used for EGM.type = "standard". Defaults to NULL but must be set

opt

Character vector (length = 1). Fit index used to select from when searching over models (only applies to EGM.type = "search"). Available options include:

  • "AIC"

  • "BIC"

  • "CFI"

  • "chisq"

  • "logLik"

  • "RMSEA"

  • "SRMR"

  • "TEFI"

  • "TEFI.adj"

  • "TLI"

Defaults to "SRMR"

constrained

Boolean (length = 1). Whether memberships of the communities should be added as a constraint when optimizing the network loadings. Defaults to TRUE which ensures assigned loadings are guaranteed to never be smaller than any cross-loadings. Set to FALSE to freely estimate each loading similar to exploratory factor analysis

verbose

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, community.unidimensional, EGA, and net.loads

Author

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

Examples

# Get depression data
data <- depression[,24:44]

# Estimate EGM (using EGA)
egm_ega <- EGM(data)

# Estimate EGM (using EGA) specifying communities
egm_ega_communities <- EGM(data, communities = 3)

# Estimate EGM (using EGA) specifying structure
egm_ega_structure <- EGM(
  data, structure = c(
    1, 1, 1, 2, 1, 1, 1,
    1, 1, 1, 3, 2, 2, 2,
    2, 3, 3, 3, 3, 3, 2
  )
)

# Estimate EGM (using standard)
egm_standard <- EGM(
  data, EGM.model = "standard",
  communities = 3, # specify number of communities
  p.in = 0.95, # probability of edges *in* each community
  p.out = 0.80 # probability of edges *between* each community
)

if (FALSE) { # \dontrun{
# Estimate EGM (using EGA search)
egm_ega_search <- EGM(
  data, EGM.model = "EGA", search = TRUE
)

# Estimate EGM (using EGA search and AIC criterion)
egm_ega_search_AIC <- EGM(
  data, EGM.model = "EGA", search = TRUE, opt = "AIC"
)

# Estimate EGM (using search)
egm_search <- EGM(
  data, EGM.model = "standard", search = TRUE,
  communities = 3, # need communities or structure
  p.in = 0.95 # only need 'p.in'
)} # }