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Estimate a Single Network

Source: R/estimatenets.R
estimate_single_network.Rd

Estimates a single psychological network from a data frame using regularized partial correlations (via bootnet) and Mixed Graphical Models (via mgm). Provides network visualization with predictability metrics (R-squared pie charts).

Usage

estimate_single_network(
  df,
  Vars = NULL,
  layout = "circle",
  color = DEFAULT_NODE_COLOR,
  title = "Between-person network",
  default = "EBICglasso",
  var_types = NULL,
  qgraph_args = list(),
  network_args = list(),
  verbose = TRUE,
  compute_centrality = TRUE
)

Arguments

df

A data frame containing the variables to be analyzed.

Vars

A character vector specifying the names of variables to include. If NULL (default), all columns of the data frame are used.

layout

Character string specifying the layout algorithm for the network plot. Options: "circle", "spring", "groups", etc. Defaults to "circle".

color

Character string or vector specifying node colors. Defaults to "#EEEEEE".

title

Title for the network plot. Defaults to "Between-person network".

default

Network estimation method for bootnet::estimateNetwork. Options: "EBICglasso" (default), "ggmModSelect", "pcor", etc.

var_types

Character vector of variable types for MGM ("g" = Gaussian, "c" = categorical, "p" = Poisson). If NULL (default), types are auto-detected.

qgraph_args

List of additional arguments passed to qgraph::qgraph.

network_args

List of additional arguments passed to bootnet::estimateNetwork.

verbose

Logical. If TRUE, prints progress messages. Defaults to TRUE.

compute_centrality

Logical. If TRUE, computes centrality measures. Defaults to TRUE.

Value

A list of class "network_analysis" containing:

  • network_object: Full network object from bootnet

  • network_matrix: Adjacency/weight matrix

  • prediction: MGM prediction results (R2, RMSE)

  • centrality: Centrality measures (if compute_centrality = TRUE)

  • qgraph: The qgraph plot object

  • var_types: Variable types used for MGM

Examples

if (FALSE) { # \dontrun{
# Basic usage
result <- estimate_single_network(
  df = my_data,
  Vars = c("var1", "var2", "var3", "var4", "var5")
)

# Access network matrix
result$network_matrix

# View predictability
result$prediction

# View centrality
result$centrality

# Custom settings
result <- estimate_single_network(
  df = my_data,
  Vars = c("var1", "var2", "var3"),
  layout = "spring",
  default = "ggmModSelect",
  var_types = c("g", "g", "c"),  # Manual type specification
  verbose = FALSE
)
} # }