UVA {EGAnet} R Documentation

## Unique Variable Analysis

### Description

Identifies redundant variables in a multivariate dataset using a number of different association methods and types of significance values (see Christensen, Garrido, & Golino, 2020 for more details)

### Usage

UVA(
data,
n = NULL,
model = c("glasso", "TMFG"),
corr = c("cor_auto", "pearson", "spearman"),
method = c("cor", "pcor", "wTO"),
sig,
key = NULL,
reduce = TRUE,
auto = TRUE,
label_latent = TRUE,
reduce.method = c("latent", "remove", "sum"),
lavaan.args = list(),
plot.redundancy = FALSE,
plot.args = list()
)


### Arguments

 data Matrix or data frame. Input can either be data or a correlation matrix n Numeric. If input in data is a correlation matrix, then sample size is required. Defaults to NULL 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 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. method Character. Computes weighted topological overlap ("wTO" using EBICglasso), partial correlations ("pcor"), or correlations ("cor") Defaults to "wTO" type Character. Type of significance. Computes significance using the standard p-value ("alpha"), adaptive alpha p-value (adapt.a), or some threshold "threshold". Defaults to "threshold" sig Numeric. p-value for significance of overlap (defaults to .05). Defaults for "threshold" for each method: "wTO" .25 "pcor" .35 "cor" .50 key Character vector. A vector with variable descriptions that correspond to the order of variables input into data. Defaults to NULL or the column names of data reduce Boolean. Should redundancy reduction be performed? Defaults to TRUE. Set to FALSE for redundancy analysis only auto Boolean. Should redundancy reduction be automated? Defaults to TRUE. Set to FALSE for manual selection label_latent Boolean. Should latent variables be labelled? Defaults to TRUE. Set to FALSE for arbitrary labelling (i.e., "LV_") reduce.method Character. How should data be reduced? Defaults to "latent" "latent" Redundant variables will be combined into a latent variable "remove" All but one redundant variable will be removed "sum" Redundant variables are combined by summing across cases (rows) lavaan.args List. If reduce.method = "latent", then lavaan's cfa function will be used to create latent variables to reduce variables. Arguments should be input as a list. Some example arguments (see lavOptions for full details): estimator Estimator to use for latent variables (see Estimators) for more details. Defaults to "MLR" for continuous data and "WLSMV" for mixed and categorical data. Data are considered continuous data if they have 6 or more categories (see Rhemtulla, Brosseau-Liard, & Savalei, 2012) missing How missing data should be handled. Defaults to "fiml" std.lv If TRUE, the metric of each latent variable is determined by fixing their (residual) variances to 1.0. If FALSE, the metric of each latent variable is determined by fixing the factor loading of the first indicator to 1.0. If there are multiple groups, std.lv = TRUE and "loadings" is included in the group.label argument, then only the latent variances i of the first group will be fixed to 1.0, while the latent variances of other groups are set free. Defaults to TRUE adhoc Boolean. Should adhoc check of redundancies be performed? Defaults to TRUE. If TRUE, adhoc check will run the redundancy analysis on the reduced variable set to determine if there are any remaining redundancies. This check is performed with the arguments: method = "wTO", type = "threshold", and sig = .20. This check is based on Christensen, Garrido, and Golino's (2020) simulation where these parameters were found to be the most conservative, demonstrating few false positives and false negatives plot.redundancy Boolean. Should redundancies be plotted in a network plot? Defaults to FALSE plot.args List. Arguments to be passed onto ggnet2. Defaults: vsize = 6Changes node size alpha = 0.4Changes transparency label.size = 5Changes label size edge.alpha = 0.7Changes edge transparency

### Value

Returns a list:

 redundancy A list containing several objects: redudant Vectors nested within the list corresponding to redundant nodes with the name of object in the list data Original data correlation Correlation matrix of original data weights Weights determine by weighted topological overlap, partial correlation, or zero-order correlation network If method = "wTO", then the network computed following EGA with EBICglasso network estimation plot If redundancy.plot = TRUE, then a plot of all redundancies found descriptives basic A vector containing the mean, standard deviation, median, median absolute deviation (MAD), 3 times the MAD, 6 times the MAD, minimum, maximum, and critical value for the overlap measure (i.e., weighted topological overlap, partial correlation, or threshold) centralTendency A matrix for all (absolute) non-zero values and their respective standard deviation from the mean and median absolute deviation from the median method Returns method argument type Returns type argument distribution If type != "threshold", then distribution that was used to determine significance reduced If reduce = TRUE, then a list containing: data New data with redundant variables merged or removed mergedA matrix containing the variables that were decided to be redundant with one another methodMethod used to perform redundancy reduction adhoc If adhoc = TRUE, then the adhoc check containing the same objects as in the redundancy list object in the output

### Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

### References

# Simulation using UVA
Christensen, A. P., Garrido, L. E., & Golino, H. (under review). Unique Variable Analysis: A novel approach for detecting redundant variables in multivariate data. PsyArXiv.

# Implementation of UVA (formally node.redundant)
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, 1095-1108.

# wTO measure
Nowick, K., Gernat, T., Almaas, E., & Stubbs, L. (2009). Differences in human and chimpanzee gene expression patterns define an evolving network of transcription factors in brain. Proceedings of the National Academy of Sciences, 106, 22358-22363.

# Selection of CFA Estimator
Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17, 354-373.

### Examples

# Select Five Factor Model personality items only
idx <- na.omit(match(gsub("-", "", unlist(psychTools::spi.keys[1:5])), colnames(psychTools::spi)))
items <- psychTools::spi[,idx]

# Change names in redundancy output to each item's description
key.ind <- match(colnames(items), as.character(psychTools::spi.dictionary$item_id)) key <- as.character(psychTools::spi.dictionary$item[key.ind])

# Automated selection of redundant variables (default)

uva.results <- UVA(data = items, key = key)

# Manual selection of redundant variables
if(interactive()){
uva.results <- UVA(data = items, key = key, type = "adapt")
}



[Package EGAnet version 1.1.0 Index]