Title:	Methods and Measures for Semantic Network Analysis
Version:	1.4.4
Date:	2023-08-10
Maintainer:	Alexander P. Christensen <alexpaulchristensen@gmail.com>
Description:	Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf), and a function to compute global network measures. Significance tests and plotting features are also implemented.
Depends:	R (≥ 3.6.0)
License:	GPL (≥ 3.0)
Encoding:	UTF-8
LazyData:	true
Imports:	pbapply, dplyr, plyr, magrittr, ggplot2, igraph, qgraph, scales, car, broom, effects, methods, philentropy
URL:	https://github.com/AlexChristensen/SemNeT
BugReports:	https://github.com/AlexChristensen/SemNeT/issues
NeedsCompilation:	no
Suggests:	shiny, shinyjs, shinyalert, shinyMatrix, shinyBS, spreadr, animation, R.matlab, foreign, readxl, data.table, NetworkToolbox, SemNetCleaner, SemNetDictionaries
RoxygenNote:	7.2.3
Packaged:	2023-08-10 17:38:44 UTC; chria12
Author:	Alexander P. Christensen [aut, cre], Yoed N. Kenett [aut, ctb]
Repository:	CRAN
Date/Publication:	2023-08-12 15:50:13 UTC

SemNeT–package

Description

Implements several functions for the analysis of semantic networks including partial node bootstraping (Kenett, Anaki, & Faust, 2014), random walk simulation (Kenett & Austerweil, 2016), and a function to compute global network measures. Significance tests and plotting features are also implemented.

Author(s)

Alexander P. Christensen <alexpaulchristensen@gmail.com> & Yoed N. Kenett <yoedkenett@gmail.com>

References

Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480-492.

Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8, 407.

Kenett, Y. N., & Austerweil, J. L. (2016). Examining search processes in low and high creative individuals with random walks. In Paper presented at the proceedings of the 38th annual meeting of the cognitive science society. Austin, TX.

See Also

Useful links:

• https://github.com/AlexChristensen/SemNeT

• Report bugs at https://github.com/AlexChristensen/SemNeT/issues

Average Shortest Path Length

Description

Computes the global average shortest path length of the network

Usage

ASPL(A, weighted = FALSE)

Arguments

Value

Returns the ASPL of the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(similarity(sim.fluency(100), method = "cor"))

# Unweighted
aspl <- ASPL(A)

Clustering Coefficient

Description

Computes global clustering coefficient CC

Usage

CC(A, weighted = FALSE)

Arguments

Value

Returns the network's CC

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(similarity(sim.fluency(100), method = "cor"))

# Unweighted
cc <- CC(A)

Community Network Estimation

Description

Estimates a semantic network using the Community Network method described in Goni et al. (2011)

Usage

CN(data, window = 2, alpha = 0.05, enrich = FALSE)

Arguments

Value

Returns a undirected semantic network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Goni, J., Arrondo, G., Sepulcre, J., Martincorena, I., de Mendizabal, N. V., Corominas-Murtra, B., ... & Villoslada, P. (2011). The semantic organization of the animal category: Evidence from semantic verbal fluency and network theory. Cognitive Processing, 12, 183-196.

Examples

# Get data
data <- open.clean

# Organize group data
## Get group data
group <- open.group

## Low and high openness to experience groups
low <- data[which(group == "Low"),]
high <- data[which(group == "High"),]

## Not run: 
# Compute networks
low.net <- CN(low)
high.net <- CN(high)

## End(Not run)

Naive Random Walk Network Estimation

Description

Estimates a semantic network using the Naive Random Walk method described in Lerner, Ogrocki, and Thomas (2009)

Usage

NRW(data, type = c("num", "prop"), threshold = 0)

Arguments

Value

Returns a undirected semantic network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Lerner, A. J., Ogrocki, P. K., & Thomas, P. J. (2009). Network graph analysis of category fluency testing. Cognitive and Behavioral Neurology, 22, 45-52.

Examples

# Get data
data <- open.clean

# Organize group data
## Get group data
group <- open.group

## Low and high openness to experience groups
low <- data[which(group == "Low"),]
high <- data[which(group == "High"),]

# Compute networks
low.net <- NRW(low)
high.net <- NRW(high)

Pathfinder Network

Description

Estimates a pathfinder network using the MST-Pathfinder Network method from Quirin et al. (2008; see also Schvaneveldt, 1990)

Usage

PF(data)

Arguments

Value

An adjacency matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Quirin, A., Cordon, O., Guerrero-Bote, V. P., Vargas-Quesada, B., & Moya-Aneon, F. (2008) A quick MST-based algorithm to obtain Pathfinder networks (Inf, n-1). Journal of the American Society for Information Science and Technology, 59, 1912-1924.

Schvaneveldt, R. W. (1990). Pathfinder associative networks: Studies in knowledge organization. Norwood, NJ: Ablex Publishing.

Examples

# Obtain data
data <- open.binary

# Estimate network
pf.net <- PF(data)

Modularity

Description

Computes a global modularity measure (Q) using the Louvain community detection algorithm

Usage

Q(A)

Arguments

Value

Returns Q or a measure of how well the communities in the network are compartmentalized

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, P10008.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(similarity(sim.fluency(100), method = "cor"))

modularity <- Q(A)

Shiny App for SemNeT

Description

An interactive Shiny application for running SemNeT analysis.

Usage

SemNeTShiny()

Value

A list called resultShiny containing:

(see semnetmeas)

(see compare_nets)

Examples

if(interactive())
{SemNeTShiny()}

Triangulated Maximally Filtered Graph

Description

Applies the Triangulated Maximally Filtered Graph (TMFG) filtering method (Please see and cite Massara et al., 2016). The TMFG method uses a structural constraint that limits the number of zero-order correlations included in the network (3n - 6; where n is the number of variables). The TMFG algorithm begins by identifying four variables which have the largest sum of correlations to all other variables. Then, it iteratively adds each variable with the largest sum of three correlations to nodes already in the network until all variables have been added to the network. This structure can be associated with the inverse correlation matrix (i.e., precision matrix) to be turned into a GGM (i.e., partial correlation network) by using LoGo. See Details for more information on this network estimation method.

Usage

TMFG(data, depend = FALSE)

Arguments

Details

The TMFG method applies a structural constraint on the network, which restrains the network to retain a certain number of edges (3n-6, where n is the number of nodes; Massara et al., 2016). The network is also composed of 3- and 4-node cliques (i.e., sets of connected nodes; a triangle and tetrahedron, respectively). The TMFG method constructs a network using zero-order correlations and the resulting network can be associated with the inverse covariance matrix (yielding a GGM; Barfuss, Massara, Di Matteo, & Aste, 2016). Notably, the TMFG can use any association measure and thus does not assume the data is multivariate normal.

Construction begins by forming a tetrahedron of the four nodes that have the highest sum of correlations that are greater than the average correlation in the correlation matrix. Next, the algorithm iteratively identifies the node that maximizes its sum of correlations to a connected set of three nodes (triangles) already included in the network and then adds that node to the network. The process is completed once every node is connected in the network. In this process, the network automatically generates what's called a planar network. A planar network is a network that could be drawn on a sphere with no edges crossing (often, however, the networks are depicted with edges crossing; Tumminello, Aste, Di Matteo, & Mantegna, 2005).

Value

Returns an TMFG filtered adjacency matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018). Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches. Behavior Research Methods, 50, 2531-2550.

Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulated maximally filtered graph. Journal of Complex Networks, 5, 161-178.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(similarity(sim.fluency(100), method = "cor"))

Frequency of Animal Responses

Description

Frequency of animal responses from Christensen & Kenett (2019). These frequencies are used to generate data in the sim.fluency function.

Usage

data(animals.freq)

Format

animals.freq (vector, length = 367)

Examples

data("animals.freq")

Bootstrapped Semantic Network Analysis

Description

Bootstrap techniques to generate semantic networks and compute global network characteristics

Usage

bootSemNeT(
  ...,
  method = c("CN", "NRW", "PF", "TMFG"),
  methodArgs = list(),
  type = c("case", "node"),
  prop = 0.5,
  sim,
  weighted = FALSE,
  iter = 1000,
  cores
)

Arguments

Value

Returns a list containing:

If a paired network is input, then also returns:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Dataset
one <- sim.fluency(20)

# Run bootstrap node-drop (partial) networks
one.result <- bootSemNeT(one, prop = .50, iter = 100,
sim = "cosine", cores = 2, method = "TMFG", type = "node")

# Run bootstrap case-drop networks
## Includes additional equating argument: minCase
one.result <- bootSemNeT(one, iter = 100, sim = "cosine",
cores = 2, method = "TMFG", type = "case", methodArgs = list(minCase = 2))


# Bootstrap case-wise networks
## Get openness data
low <- open.clean[which(open.group == "Low"),]
high <- open.clean[which(open.group == "High"),]

## Run
### Inlcudes additional NRW argument: threshold
open <- bootSemNeT(low, high, iter = 100, cores = 2, method = "NRW", type = "case",
methodArgs = list(type = "num", threshold = 3))

Plots Networks for Comparison

Description

Uses qgraph to plot networks. Accepts any number of networks and will organize the plots in the number of side-by-side columns using the heuristic of taking the square root of the number of input and rounding down to the nearest integer (i.e., floor(sqrt(length(input)))).

Examples

• 3 networks: 1 x 3

• 6 networks: 2 x 3

• 9 networks: 3 x 3

Usage

compare_nets(
  ...,
  title,
  config,
  placement = c("match", "default"),
  weighted = FALSE,
  qgraph.args = list()
)

Arguments

Value

Plots networks using qgraph

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1-18.

Jones, P. J. (2019). networktools: Tools for Identifying Important Nodes in Networks. R package version 1.2.1.

Jones, P. J., Mair, P., & McNally, R. (2018). Visualizing psychological networks: A tutorial in R. Frontiers in Psychology, 9, 1742.

Examples

# Simulate Datasets
one <- sim.fluency(10)
two <- sim.fluency(10)

# Compute similarity matrix
cos1 <- similarity(one, method = "cosine")
cos2 <- similarity(two, method = "cosine")

# Compute networks
net1 <- TMFG(cos1)
net2 <- TMFG(cos2)

# Compare networks
compare_nets(net1, net2, title = list("One", "Two"), config = "spring")

# Change edge colors
compare_nets(net1, net2, title = list("One", "Two"),
config = "spring", qgraph.args = list(edge.color = "blue"))

Convert Adjacency Matrix to Cytoscape Format

Description

Converts an adjacency matrix to Cytoscape's sparse matrix format

Usage

convert2cytoscape(A)

Arguments

Value

A sparse matrix formatted for Cytoscape

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., ... & Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13, 2498-2504.

Examples

# Simulate Datasets
one <- sim.fluency(10)
two <- sim.fluency(10)

# Compute similarity matrix
cos1 <- similarity(one, method = "cosine")
cos2 <- similarity(two, method = "cosine")

# Compute networks
net1 <- TMFG(cos1)
net2 <- TMFG(cos2)

# Convert to Cytoscape format
cyto1 <- convert2cytoscape(net1)
cyto2 <- convert2cytoscape(net2)

# Write to .csv
write.csv(cyto1, file.path(tempdir(), "cyto1.csv"), row.names = FALSE)
write.csv(cyto2, file.path(tempdir(), "cyto2.csv"), row.names = FALSE)

Convert Network(s) to igraph's Format

Description

Converts single or multiple networks into igraph's format for network analysis

Usage

convert2igraph(A, neural = FALSE)

Arguments

Value

Returns a network matrix in igraph's format

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(similarity(sim.fluency(50), method = "cor"))

igraphNetwork <- convert2igraph(A)

Equate Groups

Description

A function to "equate" multiple response matrices to one another. N number of groups are matched based on their responses so that every group has the same responses in their data

Usage

equate(...)

Arguments

Value

This function returns a list containing the equated binary response matrices in the order they were input. The response matrices are labeled as the object name they were entered with

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Obtain binary data
bin <- open.binary

# Finalize mat1
mat1 <- finalize(bin[c(1:5),])

# Finalize mat2
mat2 <- finalize(bin[c(6:10),])

# Equate mat1 and mat1
eq <- equate(mat1, mat2)

# Obtain respective equated response matrices
eq.mat1 <- eq$mat1 # list objects are named with the names
eq.mat2 <- eq$mat2 # they were entered with

Finalize Response Matrix

Description

Finalizes the response matrix by keeping responses that are given by a certain number of people

Usage

finalize(rmat, minCase = 2)

Arguments

Value

A binary response matrix with responses given by at least minCase people

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Obtain binary data
bin <- open.binary

# Finalize mat1
mat1 <- finalize(bin)

High Openness to Experience Network

Description

High openness to experience network from Christensen & Kenett (2019)

Usage

data(net.high)

Format

net.high (matrix, 160 x 160)

References

Christensen, A. P., & Kenett, Y. N. (2019) Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. PsyArXiv.

Examples

data("net.high")

Low Openness to Experience Network

Description

Low openness to experience network from Christensen & Kenett (2019)

Usage

data(net.low)

Format

net.low (matrix, 160 x 160)

References

Christensen, A. P., & Kenett, Y. N. (2019) Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. PsyArXiv.

Examples

data("net.low")

Simulated Result for Dataset One

Description

A result of bootSemNeT from a simulated dataset

Usage

data(one.result)

Format

one.result (list, length = 4)

Examples

data("one.result")

Binary response Matrices (Openness and Verbal Fluency)

Description

Binary response matrices for the Animals verbal fluency data (n = 516) from Christensen et al. (2018).

Usage

data(open.binary)

Format

open.binary (matrix, 516 x 367)

References

Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480-492.

Examples

data("open.binary")

Cleaned response Matrices (Openness and Verbal Fluency)

Description

Cleaned response matrices for the Animals verbal fluency data (n = 516) from Christensen et al. (2018).

Usage

data(open.clean)

Format

open.clean (matrix, 516 x 35)

References

Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480-492.

Examples

data("open.clean")

Groups for Openness and Verbal Fluency

Description

Groups for the Animals verbal fluency data (n = 516) from Christensen et al. (2018; see also open.clean).

Usage

data(open.group)

Format

open.group (vector, length = 516)

References

Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480-492.

Examples

data("open.group")

Animate Networks for Spreading Activation from Shiny

Description

Uses qgraph and ani.record to animate networks. Accepts only one network animation at a time

Usage

## S3 method for class 'animateShiny'
plot(x, ...)

Arguments

Value

Plots animated networks using qgraph and ani.record

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1-18. Retrieved from: http://www.jstatsoft.org/v48/i04/

Siew, C. S. Q. (2019). spreadr: An R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. https://doi.org/10.3758/s13428-018-1186-5

Examples

if(interactive())
{SemNeTShiny()}

## Not run: 
  plot(resultShiny$spreadingActivationPlot[[1]])

## End(Not run)

Plot for bootSemNeT

Description

Plots output from bootSemNeT

Usage

## S3 method for class 'bootSemNeT'
plot(..., groups = NULL, measures = c("ASPL", "CC", "Q"))

Arguments

Value

Returns plots for the specified measures

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Dataset
one <- sim.fluency(20)

# Run partial bootstrap networks
one.result <- bootSemNeT(one, prop = .50, iter = 1000,
sim = "cosine", cores = 2, type = "node", method = "TMFG")

# Plot
plot(one.result, groups = c("One"))

Plots Networks for Comparison from Shiny

Description

Uses qgraph to plot networks. Accepts any number of networks and will organize the plots in the number of side-by-side columns using the heuristic of taking the square root of the number of input and rounding down to the nearest integer (i.e., floor(sqrt(length(input)))). Performs the same operations as compare_nets

Examples

• 3 networks: 1 x 3

• 6 networks: 2 x 3

• 9 networks: 3 x 3

Usage

## S3 method for class 'compareShiny'
plot(x, ...)

Arguments

Value

Plots networks using qgraph

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1-18.

Jones, P. J. (2019). networktools: Tools for Identifying Important Nodes in Networks. R package version 1.2.1.

Jones, P. J., Mair, P., & McNally, R. (2018). Visualizing psychological networks: A tutorial in R. Frontiers in Psychology, 9, 1742.

Examples

# Simulate Datasets
one <- sim.fluency(10)
two <- sim.fluency(10)

# Compute similarity matrix
cos1 <- similarity(one, method = "cosine")
cos2 <- similarity(two, method = "cosine")

# Compute networks
net1 <- TMFG(cos1)
net2 <- TMFG(cos2)

# Compare networks
compare_nets(net1, net2, title = list("One", "Two"), config = "spring")

# Change edge colors
compare_nets(net1, net2, title = list("One", "Two"),
config = "spring", qgraph.args = list(edge.color = "blue"))

Test Against Random Networks

Description

Performs significance tests for global measures of semantic networks against the global measures of equivalent size (and density) random networks

Usage

randnet.test(..., iter, cores)

Arguments

Value

Returns a matrix containing p-values for the network measures of the input networks against the distribution of equivalent random networks. The last two columns contain the mean ("M.rand") and standard deviation ("SD.rand") of the network measures for the random network distribution

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Viger, F., & Latapy, M. (2016). Efficient and simple generation of random simple connected graphs with prescribed degree sequence. Journal of Complex Networks, 4, 15-37.

Examples

# Get openness data
one <- open.clean[which(open.group == "Low"),]
two <- open.clean[which(open.group == "High"),]

# Compute networks
net.one <- CN(one)
net.two <- CN(two)

# Perform random networks test
randnet.test(net.one, net.two, iter = 100, cores = 2)

Random Walk Simulation

Description

Simulates random walks over two networks to examine the characteristics of spontaneous spreading activation (see Kenett & Austerweil, 2016)

Usage

randwalk(A, B, reps = 20, steps = 10, iter = 10000, cores)

Arguments

Value

A result matrix containing the means and standard deviations for several measures as well as p-values for a Mann-Whitney U test

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com> and Yoed Kenett <yoedkenett@gmail.com>

References

Kenett, Y. N., & Austerweil, J. L. (2016). Examining search processes in low and high creative individuals with random walks. In Paper presented at the proceedings of the 38th annual meeting of the cognitive science society. Austin, TX.

Examples

# Simulate Datasets
one <- sim.fluency(10)
two <- sim.fluency(10)

# Compute similarity matrix
cos1 <- similarity(one, method = "cosine")
cos2 <- similarity(two, method = "cosine")

# Compute networks
net1 <- TMFG(cos1)
net2 <- TMFG(cos2)

# Run random walk analysis
rw.results <- randwalk(net1, net2, iter = 100, cores = 2)

Response Analysis

Description

Computes the difference in the total and unique number of responses between two groups (follows Christensen et al., 2018)

Usage

response.analysis(...)

Arguments

Value

A list containing objects:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480-492.

Examples

# Obtain data
low <- open.clean[which(open.group == "Low"),]
high <- open.clean[which(open.group == "High"),]

# Perform analysis
response.analysis(low, high)

Semantic Network Measures

Description

Computes the average shortest path length (ASPL), clustering coefficient(CC), and modularity (Q) of the network

Usage

semnetmeas(A, meas = c("ASPL", "CC", "Q"), weighted = FALSE)

Arguments

Value

Returns a values for ASPL, CC, and Q

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Datasets
one <- sim.fluency(10)

# Compute similarity matrix
cos <- similarity(one, method = "cosine")

# Compute networks
net <- TMFG(cos)

# Compute global network measures
globmeas <- semnetmeas(net)

Simulates a verbal fluency binary response matrix

Description

Simulates verbal fluency data based on the number of nodes in the desired network. The summed total of each response is simulated from a poisson distribution (see rpois), using frequencies from the animals.freq data. Using these sums, participants responses are simulated with a probability of giving a response as the total of the summed response over the number of participants.

Usage

sim.fluency(nodes, cases, random = FALSE)

Arguments

Value

A binary matrix with p (participants) by n (nodes)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate data for 50 nodes and 200 participants
sim.fluency(nodes = 50, cases = 200)

Measures of Similarity

Description

Computes several measures of similarity (see Choi, Cha, & Tappert, 2010 for additional measures)

Usage

similarity(
  data,
  method = c("angular", "cor", "cosine", "euclid", "faith", "jaccard", "phi", "rr")
)

Arguments

Value

A symmetric similarity matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Choi, S. S., Cha, S. H., & Tappert, C. C. (2010). A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics, 8, 43-48.

Examples

# Simulate Datasets
one <- sim.fluency(10)

# Compute similarity matrix
cos <- similarity(one, method = "cosine")

Statistical tests for bootSemNeT

Description

Computes statistical tests for bootstrapped networks from bootSemNeT

Usage

test.bootSemNeT(
  ...,
  test = c("ANCOVA", "ANOVA", "t-test"),
  measures = c("ASPL", "CC", "Q"),
  formula = NULL,
  groups = NULL
)

Arguments

Value

Returns a list containing the objects:

If two groups:

A matrix in each object has the following columns:

Row names refer to the proportion of nodes remaining in bootstrapped networks

If three or more groups:

A list containing two objects:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Dataset
one <- sim.fluency(20)
two <- sim.fluency(20)

# Run partial bootstrap networks
two.result <- bootSemNeT(one, two, prop = .50, iter = 100,
sim = "cosine", cores = 2, type = "node", method = "TMFG")

# Compute tests
test.bootSemNeT(two.result)


# Two-way ANOVA example
## Simulated data
hihi <- sim.fluency(50, 500)
hilo <- sim.fluency(50, 500)
lohi <- sim.fluency(50, 500)
lolo <- sim.fluency(50, 500)

## Create groups
groups <- matrix(
c("high", "high",
"high", "low",
"low", "high",
"low", "low"
), ncol = 2, byrow = TRUE)

## Change column names (variable names)
colnames(groups) <- c("gf","caq")

## Run partial bootstrap networks
boot.fifty <- bootSemNeT(hihi, hilo, lohi, lolo, prop = .50,
type = "node", method = "TMFG", cores = 2, iter = 100)
boot.sixty <- bootSemNeT(hihi, hilo, lohi, lolo, prop = .60,
type = "node", method = "TMFG", cores = 2, iter = 100)

## Compute tests
test.bootSemNeT(boot.fifty, boot.sixty,
test = "ANOVA", formula = "y ~ gf*caq", groups = groups)

Simulated Result for Dataset One and Two

Description

A result of bootSemNeT from two simulated datasets

Usage

data(two.result)

Format

two.result (list, length = 6)

Examples

data("two.result")

Plots for Vignette

Description

Plots for vignette taken from Christensen & Kenett (2019)

Usage

data(vignette.plots)

Format

vignette.plots (list, length = 3)

References

Christensen, A. P., & Kenett, Y. N. (2019) Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. PsyArXiv.

Examples

data("vignette.plots")