# Using External Networks (and external packages)

Source:`vignettes/modular-approach.Rmd`

`modular-approach.Rmd`

Although {EGAnet} features many common methods used in the network psychometric literature, it does not include all possible options. Sometimes you might switch between {EGAnet} and other packages.

### Alternative Networks

There are many different packages available to estimate psychometric
networks. One common package is {bootnet}.
`bootnet::estimateNetwork`

offers many different methods to
estimate networks. The `"ggmModSelect"`

approach will be used
as an example.

```
# Load packages
library(EGAnet); library(bootnet)
# Load data
data <- wmt2[,7:24]
# Estimate network
stepwise_result <- estimateNetwork(
data = data, default = "ggmModSelect", stepwise = TRUE
)
```

Once a network is estimated, then a modular approach to estimating
the `EGA`

workflow can be used. This workflow is the same as
what’s used internally in the `EGA`

function. If the network
is coming from an {igraph} must be
converted using the `igraph2matrix`

function (e.g.,
`ega_network <- igraph2matrix(igraph_network)`

). Any
network that is a matrix or data frame object can be used in {EGAnet}
functions like the output from `estimateNetwork`

.

#### Check for Unidimensionality

```
# Perform unidimensionality check
unidimensional_membership <- community.unidimensional(data)
# Print
unidimensional_membership
```

```
Algorithm: Louvain
Number of communities: 2
wmt1 wmt2 wmt3 wmt4 wmt5 wmt6 wmt7 wmt8 wmt9 wmt10 wmt11 wmt12 wmt13
1 1 1 1 1 2 2 2 2 1 2 2 2
wmt14 wmt15 wmt16 wmt17 wmt18
2 2 2 2 2
```

The standard unidimensional check uses the Louvain Louvain
algorithm on the zero-order correlation matrix (Christensen, 2023). The
output will include the memberships regardless of whether the data are
detected as unidimensional. In this output, the number of communities is
2 and therefore *not* unidimensional. If the number of
communities was 1, then there is no need to proceed with the
multidimensional estimation.

#### Estimate Multidimensionality

```
# Estimate multidimensionality
multidimensional_membership <- community.detection(
stepwise_result$graph, algorithm = "walktrap"
)
# Print
multidimensional_membership
```

```
Algorithm: Walktrap
Number of communities: 2
wmt1 wmt2 wmt3 wmt4 wmt5 wmt6 wmt7 wmt8 wmt9 wmt10 wmt11 wmt12 wmt13
1 1 1 1 1 1 2 2 2 1 2 2 2
wmt14 wmt15 wmt16 wmt17 wmt18
2 2 2 2 2
```

For the multidimensional estimation, the estimated network should be
used as the input. The algorithm can be set using a number of different
algorithms (see `?community.detection`

) but the default is to
use the Walktrap
algorithm. From this output, Walktrap estimates 2 communities.

#### Obtain Final Memberships

A shortcut to obtain the final memberships as is used in
`EGA`

is provided below:

```
wc <- EGAnet:::swiftelse(
# Check for whether unidimensional membership should be used
EGAnet:::unique_length(unidimensional_membership) == 1,
unidimensional_membership, multidimensional_membership
# Otherwise, use multidimensional membership
)
```

#### Plot

In order to plot using {EGAnet}, the network and memberships need to
be set up as an `EGA`

class object:

#### Modularity

Modularity can also be computed using the estimated network and appropriate memberships.

`modularity(stepwise_result$graph, wc)`

`[1] 0.204308`

### Alternative Similarity Measures

By default, {EGAnet} uses the `auto.correlate`

function to
compute appropriate correlations for each set of pairwise variables.
Other measures might need to be used in {EGAnet} functions.

#### Cosine Similarity

A common example is from natural language processing and cognitive science where cosine similarity is a common association measure between two terms. Below, we’ll show a semantic network example from the {SemNeT} package.

```
# Load packages
library(EGAnet); library(SemNeT)
# Compute cosine similarity
animals_cosine <- similarity(open.binary, method = "cosine")
```

This data are from participants who performed a verbal fluency task
and generated animals for 1 minute. Each row represents a participant
and each column represents an animal. A `1`

is an animal a
participant provided; a `0`

is an animal a participant did
not provide.

The `animals_cosine`

object is a symmetric matrix that
represents the cosine similarity between each animal. To use the cosine
similarity matrix (or any other alternative similarity matrix) in an
{EGAnet} function, `n`

or the number of cases must be
set: