bootEGA {EGAnet} R Documentation

Dimension Stability Analysis of EGA

Description

bootEGA Estimates the number of dimensions of n bootstraps using the empirical (partial) correlation matrix (parametric) or resampling from the empirical dataset (non-parametric). It also estimates a typical median network structure, which is formed by the median or mean pairwise (partial) correlations over the n bootstraps.

Usage

bootEGA(
data,
n = NULL,
uni.method = c("expand", "LE"),
iter,
type = c("parametric", "resampling"),
seed = 1234,
corr = c("cor_auto", "pearson", "spearman"),
model = c("glasso", "TMFG"),
model.args = list(),
algorithm = c("walktrap", "louvain"),
algorithm.args = list(),
typicalStructure = TRUE,
plot.typicalStructure = TRUE,
plot.type = c("GGally", "qgraph"),
plot.args = list(),
ncores,
...
)


Arguments

 data Matrix or data frame. Includes the variables to be used in the bootEGA analysis n Integer. Sample size if data provided is a correlation matrix uni.method Character. What unidimensionality method should be used? Defaults to "LE". Current options are: expand Expands the correlation matrix with four variables correlated .50. If number of dimension returns 2 or less in check, then the data are unidimensional; otherwise, regular EGA with no matrix expansion is used. This is the method used in the Golino et al. (2020) Psychological Methods simulation. LE Applies the leading eigenvalue algorithm (cluster_leading_eigen) on the empirical correlation matrix. If the number of dimensions is 1, then the leading eigenvalue solution is used; otherwise, regular EGA is used. This is the final method used in the Christensen, Garrido, and Golino (2021) simulation. iter Numeric integer. Number of replica samples to generate from the bootstrap analysis. At least 500 is recommended type Character. A string indicating the type of bootstrap to use. Current options are: "parametric" Generates n new datasets (multivariate normal random distributions) based on the original dataset, via the mvrnorm function "resampling" Generates n random subsamples of the original data seed Numeric. Seed to reproduce results. Defaults to 1234. For random results, set to NULL corr 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. model Character. A string indicating the method to use. 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 model.args List. A list of additional arguments for EBICglasso.qgraph or TMFG algorithm 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 algorithm.args List. A list of additional arguments for cluster_walktrap, cluster_louvain, or some other community detection algorithm function (see examples) typicalStructure Boolean. If TRUE, returns the typical network of partial correlations (estimated via graphical lasso or via TMFG) and estimates its dimensions. The "typical network" is the median of all pairwise correlations over the n bootstraps. Defaults to TRUE plot.typicalStructure Boolean. If TRUE, returns a plot of the typical network (partial correlations), which is the median of all pairwise correlations over the n bootstraps, and its estimated dimensions. Defaults to TRUE plot.type Character. Plot system to use. Current options are qgraph and GGally. Defaults to "GGally". plot.args List. A list of additional arguments for the network plot. For plot.type = "qgraph": vsize Size of the nodes. Defaults to 6. For plot.type = "GGally" (see ggnet2 for full list of arguments): vsize Size of the nodes. Defaults to 6. label.size Size of the labels. Defaults to 5. alpha The level of transparency of the nodes, which might be a single value or a vector of values. Defaults to 0.7. edge.alpha The level of transparency of the edges, which might be a single value or a vector of values. Defaults to 0.4. legend.names A vector with names for each dimension color.palette The color palette for the nodes. For custom colors, enter HEX codes for each dimension in a vector. See color_palette_EGA for more details and examples ncores 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 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

Value

Returns a list containing:

 iter Number of replica samples in bootstrap boot.ndim Number of dimensions identified in each replica sample boot.wc Item allocation for each replica sample bootGraphs Networks of each replica sample summary.table Summary table containing number of replica samples, median, standard deviation, standard error, 95% confidence intervals, and quantiles (lower = 2.5% and upper = 97.5%) frequency Proportion of times the number of dimensions was identified (e.g., .85 of 1,000 = 850 times that specific number of dimensions was found) EGA Output of the original EGA results typicalGraph A list containing: graph Network matrix of the median network structure typical.dim.variables An ordered matrix of item allocation wc Item allocation of the median network

Author(s)

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

References

# Original implementation of bootEGA
Christensen, A. P., & Golino, H. (2021). Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial. Psych, 3(3), 479-500.

# Structural consistency (see dimensionStability)
Christensen, A. P., Golino, H., & Silvia, P. J. (2020). A psychometric network perspective on the validity and validation of personality trait questionnaires. European Journal of Personality, 34(6), 1095-1108.

EGA to estimate the number of dimensions of an instrument using EGA and CFA to verify the fit of the structure suggested by EGA using confirmatory factor analysis.

Examples

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

# bootEGA glasso example
## plot.type = "qqraph" used for CRAN checks
## plot.type = "GGally" is the default
boot.wmt <- bootEGA(data = wmt, iter = 500, plot.type = "qgraph",
type = "parametric", ncores = 2)

# bootEGA TMFG example
boot.wmt <- bootEGA(data = wmt, iter = 500, model = "TMFG",
plot.type = "qgraph", type = "parametric", ncores = 2, seed = 1234)

# bootEGA Louvain example
boot.wmt <- bootEGA(data = wmt, iter = 500, algorithm = "louvain",
plot.type = "qgraph", type = "parametric", ncores = 2, seed = 1234)

# bootEGA Spinglass example
boot.wmt <- bootEGA(data = wmt, iter = 500, model = "TMFG", plot.type = "qgraph",
algorithm = igraph::cluster_spinglass, type = "parametric", ncores = 2)



[Package EGAnet version 1.1.0 Index]