Package {SemNeT}

Title:	Methods and Measures for Semantic Network Analysis
Version:	2.0.0
Date:	2026-09-12
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), 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:	methods, utils, stats, graphics, future, future.apply, progressr, igraph, qgraph, car, broom, effects
Suggests:	testthat (≥ 3.0.0), shiny (≥ 1.8.0), shinyjs, shinyalert, DT, promises, spreadr, shinyMatrix, shinyBS, readxl, foreign, R.matlab, tools, ggplot2
URL:	https://github.com/AlexChristensen/SemNeT
BugReports:	https://github.com/AlexChristensen/SemNeT/issues
NeedsCompilation:	yes
Config/testthat/edition:	3
Config/roxygen2/version:	8.1.0
Packaged:	2026-09-12 14:53:09 UTC; alextops
Author:	Alexander P. Christensen [aut, cre], Yoed N. Kenett [aut, ctb]
Repository:	CRAN
Date/Publication:	2026-09-12 15:10:08 UTC

SemNeT–package

Description

Implements several functions for the analysis of semantic networks including partial node bootstrapping (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

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)

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

Arguments

Value

Returns an 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
group <- open.group
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)

pf(data)

Arguments

Value

An adjacency matrix. The pre-binarization, Chebyshev-distance-weighted network is attached as the "weighted" attribute, rather than being computed and discarded

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)

q(A, resolution = 1, seed = NULL, signed = FALSE)

Arguments

Details

Community detection runs via a compiled Louvain implementation (Blondel et al., 2008) whose internal randomness depends only on seed, not on R's global random number generator – unlike igraph::cluster_louvain(), calling set.seed() beforehand has no effect on q()'s reproducibility; pass seed directly instead.

Value

Returns Q, a measure of how well the network partitions into distinct, densely-connected communities

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.

Gomez, S., Jensen, P., & Arenas, A. (2009). Analysis of community structure in networks of correlated data. Physical Review E, 80, 016114.

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
fluency_data <- matrix(sample(0:1, 1000, replace = TRUE), nrow = 100, ncol = 10)
A <- suppressWarnings(tmfg(similarity(fluency_data, method = "cor")))

modularity <- q(A, seed = 1)

Shiny App for SemNeT

Description

Launches an interactive Shiny application for semantic network analysis, covering network estimation, comparison, bootstrap and permutation testing, random walk simulation, and spreading activation. Results are retrieved via the app's own download buttons (as .rds/ .csv/image files), not returned to the R session that launched it – each browser session keeps its own results independently, so the app can be run locally or shared with multiple simultaneous users

Usage

SemNeTShiny()

Value

Invisibly returns NULL. Launches the app as a side effect; see the app's own "Export" tab for how to retrieve results

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

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). Construction begins by forming a tetrahedron from the four nodes with the largest sum of correlations, then iteratively adds each remaining node to the connected set of three nodes (a triangle) it correlates with most strongly, until every node is included.

Usage

TMFG(data, depend = FALSE)

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). The network is composed of 3- and 4-node cliques (a triangle and a tetrahedron, respectively). Notably, TMFG can use any association measure and does not assume the data is multivariate normal.

Value

Returns a 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
fluency_data <- matrix(sample(0:1, 1000, replace = TRUE), nrow = 100, ncol = 10)
A <- suppressWarnings(tmfg(similarity(fluency_data, 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")

Average Shortest Path Length

Description

Computes the global average shortest path length of a network

Usage

aspl(A, weighted = FALSE)

ASPL(A, weighted = FALSE)

Arguments

Value

Returns the network's ASPL. The per-node average shortest path length and eccentricity are attached as the "node" and "eccentricity" attributes, rather than being computed and discarded

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
fluency_data <- matrix(sample(0:1, 1000, replace = TRUE), nrow = 100, ncol = 10)
A <- suppressWarnings(tmfg(similarity(fluency_data, method = "cor")))

# Unweighted
network_aspl <- aspl(A)

Bootstrapped Semantic Network Analysis (Legacy)

Description

Backward-compatible wrapper for bootstrap_SemNeT – bootSemNeT() was renamed bootstrap_SemNeT() in SemNeT 2.0.0. The returned object gets both the old class ("bootSemNeT", so plot() still dispatches to plot.bootSemNeT) and the new one ("bootstrap_SemNeT", so it can also be passed straight into bootstrap_test_SemNeT or bootstrap_test_SemNeT's own legacy wrapper, test.bootSemNeT)

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

See bootstrap_SemNeT

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Bootstrapped Semantic Network Analysis

Description

Repeatedly re-estimates each group's semantic network from resampled data (dropping a proportion of nodes, or resampling participants with replacement) to build a bootstrap distribution of aspl, cc, and q for every group

Usage

bootstrap_SemNeT(
  ...,
  method = c("tmfg", "cn", "nrw", "pf"),
  method_args = list(),
  type = c("case", "node"),
  prop = 0.5,
  sim = "cosine",
  minimum_cases = 2,
  iter = 1000,
  cores = 1,
  keep_networks = FALSE
)

Arguments

Value

Returns a list of class "bootstrap_SemNeT":

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate datasets
one <- simulate_fluency(20)
two <- simulate_fluency(20)


# Bootstrap node-drop (partial) networks
boot_result <- bootstrap_SemNeT(
  one = one, two = two, prop = .50, iter = 100, cores = 2,
  method = "tmfg", type = "node"
)

# Bootstrap case-resampled networks -- equating (see `equate()`) requires
# named responses, so this uses real word responses instead
low <- open.clean[which(open.group == "Low"), ]
high <- open.clean[which(open.group == "High"), ]
boot_result <- bootstrap_SemNeT(
  low = low, high = high, iter = 100, cores = 2,
  method = "tmfg", type = "case", minimum_cases = 2
)

Statistical Tests for bootstrap_SemNeT

Description

Computes statistical tests comparing groups' bootstrapped network measures from one or more bootstrap_SemNeT results

Usage

bootstrap_test_SemNeT(
  ...,
  test = c("t-test", "ANOVA", "ANCOVA"),
  measures = c("aspl", "cc", "q"),
  formula = NULL,
  groups = NULL
)

Arguments

Value

Returns a list of class "bootstrap_test_SemNeT":

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

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


# Bootstrap networks
boot_result <- bootstrap_SemNeT(
  low = low, high = high, iter = 100, cores = 2,
  type = "case", method = "tmfg"
)

# Compute tests
bootstrap_test_SemNeT(boot_result)

Clustering Coefficient

Description

Computes the global clustering coefficient (CC) of a network

Usage

cc(A, weighted = FALSE)

CC(A, weighted = FALSE)

Arguments

Value

Returns the network's CC. The per-node clustering coefficient is attached as the "node" attribute, rather than being computed and discarded

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
fluency_data <- matrix(sample(0:1, 1000, replace = TRUE), nrow = 100, ncol = 10)
A <- suppressWarnings(tmfg(similarity(fluency_data, method = "cor")))

# Unweighted
network_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)

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

Arguments

Value

Returns an 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
group <- open.group
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)

Compare Graphs (Legacy)

Description

Backward-compatible wrapper for compare_networks – compare_nets() was renamed compare_networks() in SemNeT 2.0.0

Usage

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

Arguments

Value

See compare_networks

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Plot Networks for Comparison

Description

Uses qgraph to plot networks side by side. Accepts any number of networks and arranges the plots using floor(sqrt(length(...))) side-by-side columns (e.g. 3 networks: 1 row of 3; 6 networks: 2 rows of 3; 9 networks: 3 rows of 3)

Usage

compare_networks(
  ...,
  title,
  config = "spring",
  placement = c("default", "match"),
  weighted = FALSE,
  qgraph_args = list()
)

Arguments

Value

Plots networks side by side using qgraph; returns NULL invisibly

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.

Examples

# Simulate datasets
one <- simulate_fluency(10)
two <- simulate_fluency(10)

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

# Compute networks
net1 <- suppressWarnings(tmfg(cos1))
net2 <- suppressWarnings(tmfg(cos2))

# Compare networks
compare_networks(one = net1, two = net2, config = "spring")

# Change edge colors
compare_networks(
  one = net1, two = net2, config = "spring",
  qgraph_args = list(edge.color = "blue")
)

Convert an Adjacency Matrix to Cytoscape Format

Description

Converts an adjacency matrix to Cytoscape's sparse edge-list format

Usage

convert2cytoscape(A)

Arguments

Value

A sparse matrix formatted for Cytoscape, with columns nodeTO, nodeFROM, and nodeWEIGHT

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 toy binarized datasets
one <- matrix(sample(0:1, 100, replace = TRUE), nrow = 10, ncol = 10)
two <- matrix(sample(0:1, 100, replace = TRUE), nrow = 10, ncol = 10)

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

# Compute networks
net1 <- suppressWarnings(tmfg(cos1))
net2 <- suppressWarnings(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 a Network to igraph's Format

Description

Converts an adjacency matrix into igraph's network format

Usage

convert2igraph(A)

Arguments

Value

Returns a network in igraph's format

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
fluency_data <- matrix(sample(0:1, 500, replace = TRUE), nrow = 50, ncol = 10)
A <- suppressWarnings(tmfg(similarity(fluency_data, method = "cor")))

igraph_network <- convert2igraph(A)

Equate Groups

Description

Equates multiple binary response matrices to one another, so that every group retains exactly the responses common to all of them

Usage

equate(...)

Arguments

Value

A named list of the equated binary response matrices, one per input, each containing the same responses (columns) in the same order

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Obtain binary data
bin <- open.binary

# Finalize each group
mat1 <- finalize(bin[c(1:5), ])
mat2 <- finalize(bin[c(6:10), ])

# Equate the groups
eq <- equate(mat1 = mat1, mat2 = mat2)

# Obtain respective equated response matrices
eq_mat1 <- eq$mat1
eq_mat2 <- eq$mat2

Finalize a Response Matrix

Description

Finalizes a binary response matrix by keeping only responses given by at least a minimum number of participants

Usage

finalize(data, minimum_cases = 2)

Arguments

Value

A binary response matrix containing only responses given by at least minimum_cases participants

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Obtain binary data
bin <- open.binary

# Finalize
final <- finalize(bin)

Forward Flow

Description

Computes the "forward flow" of a sequence of verbal fluency or free association responses (Gray et al., 2019): for each response after the first, the mean semantic distance (1 - cosine similarity) between that response and every response that preceded it, using word vectors from a user-supplied embedding matrix.

Unlike the pre-2.0.0 version of this function, no semantic space is downloaded automatically – CRAN policy prohibits a package silently authenticating with and downloading files from an external service at runtime. Bring your own pre-trained (e.g. GloVe, word2vec) or custom word embeddings instead

Usage

forward_flow(
  response_matrix,
  embeddings,
  min_response = 3,
  max_response = NULL,
  type = c("fluency", "free"),
  cores = 1
)

Arguments

Value

For type = "fluency", a list with:

For type = "free", a list with:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com> & Brendan Baker <bsb5477@psu.edu>

References

Gray, K., Anderson, S., Chen, E. E., Kelly, J. M., Christian, M. S., Patrick, J., ... & Lewis, K. (2019). "Forward flow": A new measure to quantify free thought and predict creativity. American Psychologist, 74(5), 539-554.

Beaty, R. E., Zeitlen, D. C., Baker, B. S., & Kenett, Y. N. (2021). Forward flow and creative thought: Assessing associative cognition and its role in divergent thinking. Thinking Skills and Creativity, 100859.

Examples

# A tiny toy embedding space and toy fluency data -- illustrates the
# interface only; use real pre-trained vectors (GloVe, word2vec, etc.) in
# practice
set.seed(1)
words <- c("dog", "cat", "wolf", "car", "truck", "bus")
embeddings <- matrix(rnorm(length(words) * 5), nrow = length(words))
row.names(embeddings) <- words

responses <- matrix(
  c("dog", "wolf", "cat", "car", "truck", "bus"),
  nrow = 2, byrow = TRUE, dimnames = list(c("P1", "P2"), NULL)
)

result <- forward_flow(responses, embeddings, min_response = 2)
result$mean_flow

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")

Permutation Test for Network Measures

Description

Computes a permutation test to determine whether there's a difference in a global network measure between two samples

Usage

permutation_SemNeT(
  sample_1 = NULL,
  sample_2 = NULL,
  method = c("tmfg", "cn", "nrw", "pf"),
  method_args = list(),
  sim = "cosine",
  minimum_cases = 2,
  measure = c("aspl", "cc", "q"),
  alternative = c("two_tailed", "less", "greater"),
  iter = 1000,
  cores = 1,
  previous = NULL,
  keep_networks = FALSE
)

Arguments

Value

Returns a list of class "permutation_SemNeT":

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Get openness data -- equating (see `equate()`) requires named responses
low <- open.clean[which(open.group == "Low"), ]
high <- open.clean[which(open.group == "High"), ]


# aspl
perm_aspl <- permutation_SemNeT(
  low, high, method = "tmfg", measure = "aspl", iter = 100, cores = 2,
  keep_networks = TRUE
)

# cc, reusing the same permuted networks
perm_cc <- permutation_SemNeT(previous = perm_aspl, measure = "cc", cores = 2)

Plot for bootSemNeT (Legacy)

Description

Backward-compatible wrapper for plot.bootstrap_SemNeT – dispatches for objects returned by the legacy bootSemNeT, which carry the old class alongside the new one

Usage

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

Arguments

Value

See plot.bootstrap_SemNeT

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Plot for bootstrap_SemNeT

Description

Plots the bootstrapped distribution of each requested measure as a raincloud plot (Allen, Poggiali, Whitaker, Marshall, & Kievit, 2018) – a half (flat) violin showing the full distribution's shape, paired with a boxplot and the raw jittered bootstrap values – one panel per measure, grouped side by side

Usage

## S3 method for class 'bootstrap_SemNeT'
plot(x, measures = c("aspl", "cc", "q"), ...)

Arguments

Value

A ggplot object, returned visibly (so it prints automatically at the console, and renders correctly inside shiny::renderPlot())

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., & Kievit, R. A. (2018). Raincloud plots: a multi-platform tool for robust data visualization. PeerJ Preprints, 6, e27137v1. doi:10.7287/peerj.preprints.27137v1

Examples

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


# Bootstrap networks
boot_result <- bootstrap_SemNeT(
  low = low, high = high, iter = 100, cores = 2,
  type = "case", method = "tmfg"
)

# Plot
plot(boot_result)

Random Network Test (Legacy)

Description

Backward-compatible wrapper for random_network_test – randnet.test() was renamed random_network_test() in SemNeT 2.0.0

Usage

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

Arguments

Value

See random_network_test

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

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

random_network_test(..., iter = 1000, cores = 1)

Arguments

Value

Returns a named list (one entry per input network) of data frames with the p-value, and the random-network distribution's mean and standard deviation, for each of aspl, cc, and q

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
random_network_test(low = net_one, high = 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

random_walk(
  A,
  B,
  name_a = "A",
  name_b = "B",
  reps = 20,
  steps = 10,
  iter = 10000,
  cores = 1
)

Arguments

Value

A list with long (every simulated summary statistic, all numeric) and short (a compact, correctly-typed summary with Mann-Whitney p-values and effect directions)

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 toy binarized datasets
one <- matrix(sample(0:1, 100, replace = TRUE), nrow = 10, ncol = 10)
two <- matrix(sample(0:1, 100, replace = TRUE), nrow = 10, ncol = 10)

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

# Compute networks
net1 <- suppressWarnings(tmfg(cos1))
net2 <- suppressWarnings(tmfg(cos2))



# Run random walk analysis
rw_results <- random_walk(net1, net2, iter = 100, cores = 2)

Random Walk Simulation (Legacy)

Description

Backward-compatible wrapper for random_walk – randwalk() was renamed random_walk() in SemNeT 2.0.0

Usage

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

Arguments

Value

See random_walk

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Read a Response or Group File in Any Supported Format

Description

Multi-format file reader dispatching on a file's extension: .rds, .RData, .csv, .txt, .xls/.xlsx (via readxl), .sav (via foreign), .mat (via R.matlab), or no/unrecognized extension (read as plain delimited text). Used by the Shiny app's upload widgets, and usable directly for the same purpose outside Shiny

Usage

read_uploaded_file(datapath, filename, header = TRUE, sep = ",")

Arguments

Value

The file's contents as a data frame (or the single object it contains, for .rds/.RData)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

path <- tempfile(fileext = ".csv")
write.csv(data.frame(x = 1:3, y = c("a", "b", "c")), path, row.names = FALSE)

read_uploaded_file(path, "responses.csv")

Response Pattern Analysis (Legacy)

Description

Backward-compatible wrapper for response_analysis – response.analysis() was renamed response_analysis() in SemNeT 2.0.0

Usage

response.analysis(...)

Arguments

Value

See response_analysis

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Response Analysis

Description

Computes the difference in the total and unique number of responses between groups (follows Christensen et al., 2018). With more than two groups, every pairwise comparison is returned, since both underlying tests (a two-sample t-test and McNemar's test) are inherently pairwise

Usage

response_analysis(...)

Arguments

Value

With exactly two groups, a list with:

With more than two groups, a named list of such lists, one per pair of groups (named "<group1>_vs_<group2>")

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 = low, high = high)

Convert Raw Responses to a Binary Response Matrix

Description

Converts a raw word-response matrix (participants by response order, one word per cell) into a binary (participant by vocabulary) response matrix, alongside a matching matrix recording response order

Usage

responses_to_binary(responses)

Arguments

Value

A list of class "responses_to_binary" with:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Raw fluency responses, 2 participants, up to 3 responses each
responses <- matrix(
  c("dog", "cat", NA, "cat", "wolf", "dog"),
  nrow = 2, byrow = TRUE
)

result <- responses_to_binary(responses)
result$binary
result$order

Semantic Network Measures

Description

Computes the average shortest path length (aspl), clustering coefficient (cc), and modularity (q) of a network

Usage

semantic_network_measures(
  A,
  measures = c("aspl", "cc", "q"),
  weighted = FALSE,
  seed = NULL
)

Arguments

Value

Returns a named numeric vector with one value per requested measure

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate a toy binarized dataset
one <- matrix(sample(0:1, 100, replace = TRUE), nrow = 10, ncol = 10)

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

# Compute a network
net <- suppressWarnings(tmfg(cos))

# Compute global network measures
global_measures <- semantic_network_measures(net)

Semantic Network Measures (Legacy)

Description

Backward-compatible wrapper for semantic_network_measures – semnetmeas() was renamed semantic_network_measures() in SemNeT 2.0.0

Usage

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

Arguments

Value

A named numeric vector, named to match meas (uppercase), as in the original

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Simulate Verbal Fluency Data (Legacy)

Description

Backward-compatible wrapper for simulate_fluency – sim.fluency() was renamed simulate_fluency() in SemNeT 2.0.0

Usage

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

Arguments

Value

See simulate_fluency

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Measures of Similarity

Description

Computes a similarity matrix between the columns of a binarized verbal fluency or linguistic dataset (see Choi, Cha, & Tappert, 2010 for additional measures)

Usage

similarity(
  data,
  method = c("cosine", "angular", "cor", "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 a toy binarized dataset
one <- matrix(sample(0:1, 100, replace = TRUE), nrow = 10, ncol = 10)

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

Simulate a Verbal Fluency Binary Response Matrix

Description

Simulates verbal fluency data based on the number of nodes desired in the resulting network. Each response's total endorsement count is simulated from a Poisson distribution (see rpois), using frequencies from the animals.freq data; participants' individual responses are then simulated with a probability of endorsing each response equal to that response's simulated total over the number of participants

Usage

simulate_fluency(nodes = 100, cases = 500, random = FALSE)

Arguments

Value

A binary matrix with cases rows (participants) and nodes columns (responses)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate data for 50 nodes and 200 participants
simulate_fluency(nodes = 50, cases = 200)

Statistical Tests for bootSemNeT (Legacy)

Description

Backward-compatible wrapper for bootstrap_test_SemNeT – test.bootSemNeT() was renamed bootstrap_test_SemNeT() in SemNeT 2.0.0. Only accepts objects produced by this package's own bootSemNeT / bootstrap_SemNeT, not a raw object saved from SemNeT < 2.0.0

Usage

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

Arguments

Value

See bootstrap_test_SemNeT

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

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")