Identifies locally dependent (redundant) variables in a
multivariate dataset using the `EBICglasso.qgraph`

network estimation method and weighted topological overlap
(see Christensen, Garrido, & Golino, 2023 for more details)

## 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. Defaults to

`NULL`

- network
Symmetric matrix or data frame. A symmetric network. Defaults to

`NULL`

If both

`data`

and`network`

are provided, then`UVA`

will use the`network`

with the`data`

(rather than estimating a network from the`data`

)- n
Numeric (length = 1). Sample size if

`data`

provided is a correlation matrix. Defaults to`NULL`

- key
Character vector (length =

`ncol(data)`

). Item key for labeling variables in the results- uva.method
Character (length = 1). Whether the method described in Christensen, Garrido, and Golino (2023) publication in

*Multivariate Behavioral Research*(`"MBR"`

) or Christensen, Golino, and Silvia (2020) publication in*European Journal of Personality*(`"EJP"`

) should be used. Defaults to`"MBR"`

Based on simulation and accumulating empirical evidence, the methods described in Christensen, Golino, and Silvia (2020) such as adaptive alpha are

**outdated**. Evidence supports using a single cut-off value (regardless of continuous, polytomous, or dichotomous data; Christensen, Garrido, & Golino, 2023)- cut.off
Numeric (length = 1). Cut-off used to determine when pairwise

`wto`

values are considered locally dependent (or redundant). Must be values between`0`

and`1`

. Defaults to`0.25`

This cut-off value is

**recommended**and based on extensive simulation (Christensen, Garrido, & Golino, 2023). Printing the result will provide a gradient of pairwise redundancies in increments of 0.20, 0.25, and 0.30. Use`print`

or`summary`

on the output rather than adjusting this cut-off value- reduce
Logical (length = 1). Whether redundancies should be reduced in data. Defaults to

`TRUE`

- reduce.method
Character (length = 1). Method to reduce redundancies. Available options:

`"latent"`

— Computes latent variables using`cfa`

when there are three or more redundant variables. If variables are not all coded in the same direction, then they will be recoded as necessary. A warning will be produced for all variables that are flipped`"mean"`

— Computes mean of redundant variables. If variables are not all coded in the same direction, then they will be recoded as necessary. A warning will be produced for all variables that are flipped`"remove"`

— Removes all but one variable from a set of redundant variables`"sum"`

— Computes sum of redundant variables. If variables are not all coded in the same direction, then they will be recoded as necessary. A warning will be produced for all variables that are flipped

- auto
Logical (length = 1). Whether

`reduce`

should occur automatically. For`reduce.method = "remove"`

, the automated decision process is as follows:`Two variables`

— The variable with the lowest maximum`wto`

to all other variables (other than the one it is redundant with) is retained and the other is removed`Three or more variables`

— The variable with the highest mean`wto`

to all other variables that are redundant with one another is retained and all others are removed

- verbose
Boolean (length = 1). Whether messages and (insignificant) warnings should be output. Defaults to

`FALSE`

(silent calls). Set to`TRUE`

to see all messages and warnings for every function call- ...
Additional arguments that should be passed on to old versions of

`UVA`

or to`EGA`

and`cfa`

## References

**Most recent simulation and implementation**

Christensen, A. P., Garrido, L. E., & Golino, H. (2023).
Unique variable analysis: A network psychometrics method to detect local dependence.
*Multivariate Behavioral Research*.

**Conceptual foundation and outdated methods**

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.

**Weighted topological overlap**

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*(3), 354-373.

## Examples

```
# Perform UVA
uva.wmt <- UVA(wmt2[,7:24])
# Show summary
summary(uva.wmt)
#> Variable pairs with wTO > 0.30 (large-to-very large redundancy)
#>
#> ----
#>
#> Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
#>
#> ----
#>
#> Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
#>
#> node_i node_j wto
#> wmt2 wmt3 0.25
```