itemStability {EGAnet} R Documentation

## Item Stability Statistics from bootEGA

### Description

Based on the bootEGA results, this function computes and plots the number of times an item (variable) is estimated in the same factor/dimension as originally estimated by EGA (item.replication). The output also contains each item's replication frequency (i.e., proportion of bootstraps that an item appeared in each dimension; item.dim.rep) as well as the average network loading for each item in each dimension (item.loadings).

### Usage

itemStability(bootega.obj, IS.plot = TRUE, structure = NULL, ...)


### Arguments

 bootega.obj A bootEGA object IS.plot Should the plot be produced for item.replication? If TRUE, then a plot for the item.replication output will be produced. Defaults to TRUE structure User specified dimensionality structure. ... Additional arguments. Used for deprecated arguments from previous versions of itemStability

### Value

Returns a list containing:

 membership A list containing: empirical The empirical memberships from the empirical EGA result unique The unique dimensions from the empirical EGA result bootstrap The memberships from the replicate samples in the bootEGA results item.stability A list containing: empirical.dimensions The proportion of times each item replicated within the empirical EGA defined dimension. This EGA result is defined using the input from bootEGA all.dimensions The proportion of times each item replicated in each of the empirical EGA defined dimensions. This EGA result is defined using the input from bootEGA plot A plot of the number of times each item replicated within the empirical EGA defined dimension. mean.loadings Matrix of the average standardized network loading (computed using net.loads) for each item in each dimension

### Author(s)

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

### References

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.

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


wmt <- wmt2[,7:24]

## Not run: # Estimate EGA network
## plot.type = "qqraph" used for CRAN checks
## plot.type = "GGally" is the default
ega.wmt <- EGA(data = wmt, model = "glasso", plot.type = "qgraph")

# Estimate dimension stability
boot.wmt <- bootEGA(data = wmt, iter = 100, typicalStructure = TRUE,
plot.typicalStructure = TRUE, model = "glasso", plot.type = "qgraph",
type = "parametric", ncores = 2)

## End(Not run)

# Estimate item stability statistics
res <- itemStability(boot.wmt)

# Changing plot features (ggplot2)
## Changing colors (ignore warnings)
### qgraph Defaults
res$plot + ggplot2::scale_color_manual(values = rainbow(max(res$membership$unique))) ### Pastel res$plot +
ggplot2::scale_color_brewer(palette = "Pastel1")

## Changing Legend (ignore warnings)
res\$plot +
ggplot2::scale_color_discrete(labels = "Intelligence")



[Package EGAnet version 1.1.0 Index]