package ‘networktoolbox’ · package ‘networktoolbox ... neo-pi-3 openness to experience...

Package ‘NetworkToolbox’April 29, 2018

Title Methods and Measures for Brain, Cognitive, and PsychometricNetwork Analysis

Version 1.1.2

Date 2018-04-20

Author Alexander Christensen

Maintainer Alexander Christensen <[email protected]>

Description Implements network analysis and graph theory measures used in neuroscience, cogni-tive science, and psychology. Methods include various filtering methods and ap-proaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershogoren, Man-tegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Net-works (Barfuss, Massara, Di Mat-teo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fal-lani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods in-clude the recently developed Connectome Predictive Modeling (see references in pack-age). Also implements several network measures including local network characteris-tics (e.g., centrality), global network characteristics (e.g., clustering coefficient), and vari-ous other measures associated with the reliability and reproducibility of network analysis.

Depends R (>= 3.3.0)

License GPL (>= 3.0)

Encoding UTF-8

LazyData true

Imports Matrix, psych, corrplot, fdrtool, R.matlab, MASS, pwr, igraph,qgraph, IsingFit, ppcor, parallel, foreach, doSNOW

RoxygenNote 6.0.1.9000

NeedsCompilation no

Repository CRAN

Date/Publication 2018-04-29 21:46:26 UTC

R topics documented:NetworkToolbox-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

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2 R topics documented:

animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4behavOpen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4betweenness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5binarize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6bootgen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7bootgenPlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8centlist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9closeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10clustcoeff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11comcat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12commboot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13conn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14convert2igraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15convertConnBrainMat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16cor2cov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17cpmEV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17cpmFP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18cpmFPperm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19cpmIV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20degree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22depend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22depna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26ECO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27ECOplusMaST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28edgerep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28eigenvector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29gateway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30hybrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32is.graphical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33kld . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34lattnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35leverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35LoGo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36louvain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37MaST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38nams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39neoOpen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40neuralcorrtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41neuralgrouptest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42neuralnetfilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43neuralstat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46pathlengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47randnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48reg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

NetworkToolbox-package 3

rmse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49rspbc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50semnetboot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51semnetmeas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52sim.swn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53smallworldness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54splitsamp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55splitsampNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56splitsampStats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57stable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59TMFG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61transitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Index 64

NetworkToolbox-package

NetworkToolbox–package

Description

Implements network analysis and graph theory measures used in neuroscience, cognitive science,and psychology. Methods include various filtering methods and approaches such as threshold, de-pendency, Information Filtering Networks, and Efficiency-Cost Optimization. Brain methods in-clude the recently developed Connectome Predictive Modeling. Also implements several networkmeasures including local network characteristics (e.g., centrality), global network characteristics(e.g., clustering coefficient), and various other measures associated with the reliability and repro-ducibility of network analysis.

Author(s)

Alexander Christensen <[email protected]>

References

Christensen, A. P. (2018). NetworkToolbox: Methods and measures for brain, cognitive, and psy-chometric network analysis. R package version 1.1.2. Available from https://github.com/AlexChristensen/NetworkToolbox

https://github.com/AlexChristensen/NetworkToolbox

https://github.com/AlexChristensen/NetworkToolbox

4 behavOpen

animals Animal Verbal Fluency Data

Description

Verbal fluency reponses for the animal category. The group variable specifies people’s level ofNEO-FFI-3 Openness to Experience (low = 1, moderate = 2, high = 3).

Usage

data(animals)

Format

A 802x48 response matrix

Details

A binary verbal fluency response matrix (n = 354) from Christensen, Kenett, Cotter, Beaty, & Silvia(under review).

References

Christensen, A.P., Kenett, Y. N., Cotter, K.N., Beaty, R. E., & Silvia, P.J. (under review). Remotelyclose associations: Openness to Experience and semantic memory structure

Examples

data("animals")

behavOpen NEO-PI-3 for Resting-state Data

Description

NEO-PI-3 Openness to Experience associated with resting-state data (n = 144).

Usage

data(behavOpen)

Format

behavOpen (vector, length = 144)

betweenness 5

Details

Behavioral data of NEO-PI-3 associated with each connectivity matrix (open).

To access the resting-state brain data, please go to https://drive.google.com/file/d/1ugwi7nRrlHQYuGPzEB4wYzsizFrIMvKR/view

References

Beaty, R. E., Chen, Q., Christensen, A. P., Qiu, J., Silvia, P. J., & Schacter, D. L. (2018). Brainnetworks of the imaginative mind: Dynamic functional connectivity of default and cognitive controlnetworks relates to Openness to Experience. Human Brain Mapping, 39(2), 811-821.

Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., ... & Sil-via, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity.Proceedings of the National Academy of Sciences, 201713532.

Examples

data("behavOpen")

betweenness Betwenness Centrality

Description

Computes betweenness centrlaity of each node in a network

Usage

betweenness(A, weighted = TRUE)

Arguments

A An adjacency matrix of network data

weighted Is the network weighted? Defaults to TRUE. Set to FALSE for unweightedmeasure of betwenness centrality

Value

A vector of betweenness centrality values for each node in the network

Author(s)


References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses andinterpretations. Neuroimage, 52(3), 1059-1069.

https://drive.google.com/file/d/1ugwi7nRrlHQYuGPzEB4wYzsizFrIMvKR/view

https://drive.google.com/file/d/1ugwi7nRrlHQYuGPzEB4wYzsizFrIMvKR/view

6 binarize

Examples

A<-TMFG(neoOpen)$A

weighted_BC<-betweenness(A)

unweighted_BC<-betweenness(A,weighted=FALSE)

binarize Binarize Network

Description

Converts weighted adjacency matrix to a binarized adjacency matrix

Usage

binarize(A)

Arguments

A An adjacency matrix of network data (or an array of matrices)

Value

Returns an adjancency matrix of 1’s and 0’s

Author(s)


Examples

net<-TMFG(neoOpen)$A

psyb<-binarize(net)

bootgen 7

bootgen Bootstrapped Network Generalization

Description

Bootstraps the sample to identify the most stable correlations. Also produces a network that ispenalizes low reliability edges. This function is useful for overcoming the structural constraint ofthe IFN approach

Usage

bootgen(data, method = c("MaST", "TMFG", "LoGo", "threshold"), alpha = 0.05,n = nrow(data), iter = 1000, normal = FALSE, na.data = c("pairwise","listwise", "fiml", "none"), cores, progBar = TRUE, ...)

Arguments

data A set of data

method A network filtering method. Defaults to "TMFG"

alpha Alpha threshold bootstrapped network generalization network

n Number of people to use in the bootstrap. Defaults to full sample size

iter Number of bootstrap iterations. Defaults to 1000 iterations

normal Should data be transformed to a normal distribution? Defaults to FALSE. Datais not transformed to be normal. Set to TRUE if data should be transformed tobe normal (computes correlations using the cor_auto function)

na.data How should missing data be handled? For "listwise" deletion the na.omit func-tion is applied. Set to "fiml" for Full Information Maxmimum Likelihood (cor-Fiml). Full Information Maxmimum Likelihood is recommended but time con-suming

cores Number of computer processing cores to use for bootstrapping samples. De-faults to n - 1 total number of cores. Set to any number between 1 and maxmi-mum amount of cores on your computer

progBar Should progress bar be displayed? Defaults to TRUE. Set to FALSE for noprogress bar

... Additional arguments for filtering methods

Value

Returns a list that includes the original filtered network (orignet), correlation matrix of the meanbootstrapped network (bootmat), and unfiltered reliabilities of all of the connections (netrel)

Author(s)


8 bootgenPlot

References

Perez, M. E., & Pericchi, L. R. (2014). Changing statistical significance with the amount of infor-mation: The adaptive a significance level. Statistics & Probability Letters, 85, 20-24.

Tumminello, M., Coronnello, C., Lillo, F., Micciche, S., & Mantegna, R. N. (2007). Spanningtrees and bootstrap reliability estimation in correlation-based networks. International Journal ofBifurcation and Chaos, 17(7), 2319-2329.

Examples

## Not run:bootTMFG<-bootgen(neoOpen)

bootLoGo<-bootgen(neoOpen,method="LoGo")

bootMaST<-bootgen(neoOpen,method="MaST")

bootThreshold<-bootgen(neoOpen,method="threshold")

## End(Not run)

bootgenPlot Bootstrapped Network Generalization Plots

Description

Generates reliability plots from the bootgen function

Usage

bootgenPlot(object, bootmat = FALSE, breaks = 20)

Arguments

object An output from the bootgen function

bootmat Should the bootstrap generalization matrix be included? Defaults to FALSE. Setto TRUE to plot the original and bootmat networks’ reliablities

breaks If bootmat = TRUE, then reliability comparison histogram is produced. Thebreaks may not appear as desired, so the researcher can adjust as needed. De-faults to 20 (try 10 if 20 isn’t equivalent breaks)

Value

Returns the following plots: a reliability matrix for the original network ("Original Network Corre-lation Reliabilities" upper triangle = network reliabilites from the filtered network, lower triangle =full, unfiltered matrix reliablities), a plot of retained correlations on their reliability ("Original Net-work Correlation Strength on Reliability"), If bootmat is set to TRUE, then also a reliability matrix

centlist 9

for the bootgen network ("bootgen Network Correlation Reliabilities" upper triangle = network reli-abilites from the bootgen filtered network, lower triangle = full, unfiltered matrix reliablities), a plotof retained correlations on their reliability ("bootgen Network Correlation Strength on Reliability"),a frequency of the reliability of edges in both the original and bootgen network

Author(s)


References

Wei, T. & Simko, V.(2017). R package "corrplot": Visualization of a correlation matrix (Version0.84). Available from https://github.com/taiyun/corrplot

Examples

## Not run:bootTMFG <- bootgen(neoOpen)

bootPlot <- bootPlot(bootTMFG)

## End(Not run)

centlist List of Centrality Measures

Description

Computes centrality measures of the network

Usage

centlist(A, weighted = TRUE)

Arguments


weighted Is the network weighted? Defaults to TRUE. Set to FALSE for unweighted listof centrality measures

Value

Returns a list of betweenness, closeness, degree (weighted = strength), eigenvector, and leveragecentralities

Author(s)


https://github.com/taiyun/corrplot

10 closeness

References


Examples

A<-TMFG(neoOpen)$A

weighted_centralitylist<-centlist(A)

unweighted_centralitylist<-centlist(A, weighted=FALSE)

closeness Closeness Centrality

Description

Computes closeness centrlaity of each node in a network

Usage

closeness(A, weighted = TRUE)

Arguments

A An adjacency matrix of network dataweighted Is the network weighted? Defaults to TRUE. Set to FALSE for unweighted

measure of closeness centrality

Value

A vector of closeness centrality values for each node in the network

Author(s)


References


Examples

A<-TMFG(neoOpen)$A

weighted_LC<-closeness(A)

unweighted_LC<-closeness(A,weighted=FALSE)

clustcoeff 11

clustcoeff Clustering Coefficient

Description

Computes global clustering coefficient (CC) and local clustering coefficient (CCi)

Usage

clustcoeff(A, weighted = FALSE)

Arguments


weighted Is the network weighted? Defaults to FALSE. Set to TRUE for weighted mea-sures of CC and CCi

Value

Returns a list of CC and CCi

Author(s)


References


Examples

A<-TMFG(neoOpen)$A

unweighted_CC<-clustcoeff(A)

weighted_CC<-clustcoeff(A, weighted=TRUE)

12 comcat

comcat Communicating Nodes

Description

Computes the between-community strength for each node in the network

Usage

comcat(A, factors = c("walktrap", "louvain"), cent = c("strength","degree"), ...)

Arguments


factors Can be a vector of factor assignments or community detection algorithms ("walk-trap" or "louvain") can be used to determine the number of factors. Defaults to"walktrap". Set to "louvain" for louvain community detection

cent Centrality measure to be used. Defaults to "strength".

... Additional arguments for community detection algorithms

Value

A matrix containing the between-community strength value for each node

Author(s)


References

Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., Kerkhof, G. A., & Cramer,A. O. (2018). The role of stabilizing and communicating symptoms given overlapping communitiesin psychopathology networks. Scientific Reports, 8(1), 5854.

Examples

A<-TMFG(neoOpen)$A

communicating <- comcat(A, factors = "walktrap")

commboot 13

commboot Bootstrapped Communities Likelihood

Description

Bootstraps the sample with replace to compute walktrap reliability

Usage

commboot(data, normal = FALSE, n = nrow(data), iter = 1000,filter = c("TMFG", "threshold", "EBICglasso", "IsingFit"),method = c("louvain", "walktrap"), na.data = c("pairwise", "listwise","fiml", "none"), steps = 4, cores, ...)

Arguments

data A set of data

normal Should data be transformed to a normal distribution? Defaults to FALSE. Datais not transformed to be normal. Set to TRUE if data should be transformed tobe normal

n Number of people to use in the bootstrap. Defaults to full sample size


filter Set filter method. Defaults to "TMFG". See EBICglasso and IsingFit foradditional arguments

method Defaults to "walktrap". Set to "louvain" for the louvain community detectionalgorithm


steps Number of steps to use in the walktrap algorithm. Defaults to 4. Use a largernumber of steps for smaller networks

cores Number of computer processing cores to use for bootstrapping samples. De-faults to n - 1 total number of cores. Set to any number between 1 and maxmi-mum amount of cores on your computer

... Additional arguments for network filtering methods

Value

Returns the number of factors and their relative frequency found across bootstrapped samples

Author(s)


14 conn

References

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of com-munities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10),P10008.

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research.InterJournal, Complex Systems, 1695(5), 1-9.

Examples

## Not run:commTMFG<-commboot(neoOpen)

commThreshold<-commboot(neoOpen,filter="threshold")

## End(Not run)

conn Network Connectivity

Description

Computes the average and standard deviation of the weights in the network

Usage

conn(A)

Arguments

A An adjacency matrix of network A

Value

Returns a list of the edge weights (weights), the mean (mean), the standard deviation (sd), and thesum of the edge weights (total) in the network

Author(s)


Examples

A<-TMFG(neoOpen)$A

connectivity<-conn(A)

convert2igraph 15

convert2igraph 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

A Adjacency matrix (network matrix) or brain connectivity array (from convertConnBrainMat)

neural Is input a brain connectivity array (i.e., m x m x n)? Defaults to FALSE. Set toTRUE to convert each brain connectivity matrix

Value

Returns a network matrix in igraph’s format or returns a list of brain connectivity matrices each ofwhich have been convert to igraph’s format

Author(s)


Examples

A <- TMFG(neoOpen)$A

igraphNetwork <- convert2igraph(A)

## Not run:neuralarray<-convertConnBrainMat()

igraphNeuralList <- convert2igraph(neuralarray, neural = TRUE)

## End(Not run)

16 convertConnBrainMat

convertConnBrainMat Import CONN Toolbox Brain Matrices to R format

Description

Converts a Matlab brain z-score connectivity array (n x n x m) where n is the n x n connectivitymatrices and m is the participant. If you would like to simply import a connectivity array fromMatlab, then see the examples

Usage

convertConnBrainMat(MatlabData, progBar = TRUE)

Arguments

MatlabData Input for Matlab data file. Defaults to interactive file choice

progBar Should progress bar be displayed? Defaults to TRUE. Set FALSE for no progressbar

Value

Returns a list of correlation (rmat) and z-score (zmat) arrays of neural connectivity matrices (n x nx m)

Author(s)


Examples

## Not run:neuralarray<-convertConnBrainMat()

#Import correlation connectivity array from Matlablibrary(R.matlab)neuralarray<-readMat(file.choose())

## End(Not run)

cor2cov 17

cor2cov Convert Correlation Matrix to Covariance Matrix

Description

Converts a correlation matrix to a covariance matrix

Usage

cor2cov(cormat, data)

Arguments

cormat A correlation matrix

data The dataset the correlation matrix is from

Value

Returns a covariance matrix

Author(s)


Examples

cormat <- cor(neoOpen)

covmat <- cor2cov(cormat,neoOpen)

cpmEV Connectome-based Predictive Modeling–External Validation

Description

Applies the Connectome-based Predictive Modeling approach to neural data. This method predictsa behavioral statistic using neural connectivity from the sample. Results may differ from Matlabresults because of robust GLM methodology. This function is still in its testing phase. Please citeFinn et al., 2015; Rosenberg et al., 2016; Shen et al., 2017

Usage

cpmEV(train_na, train_b, valid_na, valid_b, thresh = 0.01, overlap = FALSE,progBar = TRUE)

18 cpmFP

Arguments

train_na Training dataset (an array from convertConnBrainMat function)

train_b Behavioral statistic for each participant for the training neural data (a vector)

valid_na Validation dataset (an array from convertConnBrainMat function)

valid_b Behavioral statistic for each participant for the validation neural data (a vector)

thresh Sets an alpha threshold for edge weights to be retained. Defaults to .01

overlap Should leave-one-out cross-validation be used? Defaults to FALSE (use fulldataset, no leave-one-out). Set to TRUE to select edges that appear in everyleave-one-out cross-validation network (time consuming)


Value

Returns a list containing a matrix (r coefficient (r), p-value (p-value), mean absolute error (mae),root mean square error (rmse)). The list also contains the positive (posMask) and negative (neg-Mask) masks used

Author(s)


References

Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris,X., Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals usingpatterns of brain connectivity. Nature Neuroscience, 18(11), 1664-1671.

Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., Chun,M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity.Nature Neuroscience, 19(1), 165-171.

Shen, X. Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., Constable,R. T. (2017). Using connectome-based predictive modeling to predict individual behavior frombrain connectivity. Nature Protocols, 12(3), 506-518.


cpmFP Connectome-based Predictive Modeling–Fingerprinting

Description

Applies the Connectome-based Predictive Modeling approach to neural data. This method identifiesindividuals based on their specific connectivity patterns. Please cite Finn et al., 2015; Rosenberget al., 2016; Shen et al., 2017


cpmFPperm 19

Usage

cpmFP(session1, session2, progBar = TRUE)

Arguments

session1 Array from convertConnBrainMat function (first session)

session2 Array from convertConnBrainMat function (second session)


Value

Returns a matrix containing the percentage and number of correctly identified subjects for sessions1 and 2

Author(s)


References




cpmFPperm Connectome-based Predictive Modeling–Fingerprinting Permutation

Description

Applies the Connectome-based Predictive Modeling approach to neural data. This method identifiesindividuals based on their specific connectivity patterns. Please cite Finn et al., 2015; Rosenberget al., 2016; Shen et al., 2017

Usage

cpmFPperm(session1, session2, iter = 1000, progBar = TRUE)

20 cpmIV

Arguments

session1 Array from convertConnBrainMat function (first session)

session2 Array from convertConnBrainMat function (second session)

iter Number of iterations to perform. Defaults to 1,000


Value

Returns a matrix containing the percentage and number of correctly identified subjects for sessions1 and 2

Author(s)


References




cpmIV Connectome-based Predictive Modeling–Internal Validation

Description

Applies the Connectome-based Predictive Modeling approach to neural data. This method predictsa behavioral statistic using neural connectivity from the sample. Please cite Finn et al., 2015;Rosenberg et al., 2016; Shen et al., 2017

Usage

cpmIV(neuralarray, bstat, covar, thresh = 0.01, method = c("mean", "sum"),model = c("linear", "quadratic", "cubic"), corr = c("pearson","spearman"), shen = FALSE, cores, progBar = TRUE)

cpmIV 21

Arguments

neuralarray Array from convertConnBrainMat function

bstat Behavioral statistic for each participant with neural data (a vector)

covar Covariates to be included in predicting relevant edges (time consuming). Mustbe input as a list() (see examples)

thresh Sets an alpha threshold for edge weights to be retained. Defaults to .01

method Use "mean" or "sum" of edge strengths in the positive and negative connec-tomes. Defaults to "mean"

model Regression model to use for fitting the data. Defaults to "linear"

corr Correlation method for assessing the relatonship between the behavioral mea-sure and edges between ROIs. Defaults to "pearson". Set to "spearman" fornon-linear or monotonic associations

shen Are ROIs from Shen et al. 2013 atlas? Defaults to FALSE. Set to TRUE forcanonical networks plot

cores Number of computer processing cores to use when performing covariate analy-ses. Defaults to n - 1 total number of cores. Set to any number between 1 andmaxmimum amount of cores on your computer


Value

Returns a list containing a matrix (correlation coefficient (r), p-value (p), mean absolute error(mae), root mean square error (rmse)). The list also contains the positive (posMask) and negative(negMask) masks, which can be visualized here: http://bisweb.yale.edu/build/connviewer.html

Author(s)


References





http://bisweb.yale.edu/build/connviewer.html

http://bisweb.yale.edu/build/connviewer.html


22 depend

degree Degree

Description

Computes degree of each node in a network

Usage

degree(A)

Arguments


Value

A vector of degree values for each node in the network. If directed network, returns a list of in-degree (inDegree), out-degree (outDegree), and relative influence (relInf)

Author(s)


References


Examples

A<-TMFG(neoOpen)$A

deg<-degree(A)

depend Dependency Network Approach

Description

Generates a dependency matrix of the data (index argument is still in testing phase)

Usage

depend(data, normal = FALSE, na.data = c("pairwise", "listwise", "fiml","none"), index = FALSE, fisher = FALSE, progBar = TRUE)

depend 23

Arguments

data A set of data



index Should correlation with the latent variable (i.e., weighted average of all vari-ables) be removed? Defaults to FALSE. Set to TRUE to remove common latentfactor

fisher Should Fisher’s Z-test be used to keep significantly higher influences (indexonly)? Defaults to FALSE. Set to TRUE to remove non-significant influences


Value

Returns an adjacency matrix of dependencies

Author(s)


References

Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E.(2010). Dominating clasp of the financial sector revealed by partial correlation analysis of the stockmarket. PloS one, 5(12), e15032.

Kenett, D. Y., Huang, X., Vodenska, I., Havlin, S., & Stanley, H. E. (2015). Partial correlationanalysis: Applications for financial markets. Quantitative Finance, 15(4), 569-578.

Examples

D<-depend(neoOpen)

Dindex<-depend(neoOpen,index=TRUE)

24 depna

depna Dependency Neural Networks

Description

Applies the dependency network approach to neural network array

Usage

depna(neuralarray, pB = TRUE, ...)

Arguments


pB Should progress bar be displayed? Defaults to TRUE. Set FALSE for no progressbar

... Additional arguments from depend function

Value

Returns an array of n x n x m dependency matrices

Author(s)


References

Jacob, Y., Winetraub, Y., Raz, G., Ben-Simon, E., Okon-Singer, H., Rosenberg-Katz, K., ... & Ben-Jacob, E. (2016). Dependency Network Analysis (DEPNA) Reveals Context Related Influence ofBrain Network Nodes. Scientific Reports, 6, 27444.

Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E.(2010). Dominating clasp of the financial sector revealed by partial correlation analysis of the stockmarket. PloS one, 5(12), e15032.

Examples

## Not run:neuralarray <- convertConnBrainMat()

dependencyneuralarray <- depna(neuralarray)

## End(Not run)

distance 25

distance Distance

Description

Computes distance matrix of the network

Usage

distance(A, weighted = FALSE)

Arguments


weighted Is the network weighted? Defaults to FALSE. Set to TRUE for weighted mea-sure of distance

Value

A distance matrix of the network

Author(s)


References


Examples

A<-TMFG(neoOpen)$A

unweighted_D<-distance(A)

weighted_D<-distance(A, weighted=TRUE)

26 diversity

diversity Diversity Coefficient

Description

Computes the diversity coefficient for each node. The diversity coefficient measures a node’s con-nections to communitites outside of its own community. Nodes that have many connections to othercommunities will have higher diversity coefficient values. Positive and negative signed weights fordiversity coefficients are computed separately.

Usage

diversity(A, factors = c("walktrap", "louvain"))

Arguments

A Network adjacency matrix

factors A vector of corresponding to each item’s factor. Defaults to "walktrap" for thewalktrap community detection algorithm. Set to "louvain" for the louvain com-munity detection algorithm. Can also be set to user-specified factors (see exam-ples)

Value

Returns a list of overall (signs not considered; overall), negative (negative), and positive (positive)gateway coefficients

Author(s)


References


Examples

#theoretical factorsfactors <- c(rep(1,8), rep(2,8), rep(3,8), rep(4,8), rep(5,8), rep(6,8))


gdiv <- diversity(A, factors = factors)

#walktrap factorswdiv <- diversity(A, factors = "walktrap")

ECO 27

ECO ECO Neural Network Filter

Description

Applies the ECO neural network filtering method

Usage

ECO(data, weighted = TRUE, directed = FALSE)

Arguments

data Can be a dataset or a correlation matrix

weighted Should network be weighted? Defaults to TRUE. Set to FALSE to produce anunweighted (binary) network

directed Is the network directed? Defaults to FALSE. Set TRUE if the network is directed

Value

A sparse association matrix

Author(s)


References

Fallani, F. D. V., Latora, V., & Chavez, M. (2017). A topological criterion for filtering informationin complex brain networks. PLoS Computational Biology, 13(1), e1005305.

Examples

## Not run:weighted_undirected_ECOnetwork<-ECO(neoOpen)

unweighted_undirected_ECOnetwork<-ECO(neoOpen,weighted=FALSE)

weighted_directed_ECOnetwork<-ECO(neoOpen,directed=TRUE)

unweighted_directed_ECOnetwork<-ECO(neoOpen,weighted=FALSE,directed=TRUE)

## End(Not run)

28 edgerep

ECOplusMaST ECO+MaST Network Filter

Description

Applies the ECO neural network filtering method combined with the MaST filtering method

Usage

ECOplusMaST(data, weighted = TRUE)

Arguments



Value


Author(s)


References


Examples

weighted_ECOplusMaSTnetwork<-ECOplusMaST(neoOpen)

edgerep Edge Replication

Description

Computes the number of edges that replicate between two cross-sectional networks

Usage

edgerep(A, B, corr = c("pearson", "spearman", "kendall"), plot = FALSE)

eigenvector 29

Arguments


B An adjacency matrix of network B

corr Correlation method for assessing the relatonship between the replicated edgeweights. Defaults to "pearson". Set to "spearman" for non-linear or monotonicassociations. Set to "kendall" for rank-order correlations

plot Should there be a plot of the replicated weights? Defaults to FALSE. Set toTRUE for a plot to be produced

Value

Returns a list of the edges that replicated and their weights (replicatedEdges), number of edges thatreplicate (replicated), total number of edges (totalEdgesA & totalEdgesB), the percentage of edgesthat replicate (percentageA & percentageB), the density of edges (densityA & densityB), the meandifference between edges that replicate (meanDifference), the sd of the difference between edgesthat replicate (sdDifference), and the correlation between the edges that replicate for both networks(correlation)

Author(s)


Examples

A<-TMFG(neoOpen)$A

B<-MaST(neoOpen)

edges<-edgerep(A,B)

eigenvector Eigenvector Centrality

Description

Computes eigenvector centrality of each node in a network

Usage

eigenvector(A, weighted = TRUE)

Arguments


weighted Is the network weighted? Defaults to TRUE. Set to FALSE for unweightedmeasure of eigenvector centrality

30 gateway

Value

A vector of eigenvector centrality values for each node in the network

Author(s)


References


Examples

A<-TMFG(neoOpen)$A

weighted_EC<-eigenvector(A)

unweighted_EC<-eigenvector(A,weighted=FALSE)

gateway Gateway Coefficient

Description

Computes the gateway coefficient for each node. The gateway coefficient measures a node’s con-nections between its community and other communities. Nodes that are solely responsible forinter-community connectivity will have higher gateway coefficient values. Positive and negativesigned weights for gateway coefficients are computed separately.

Usage

gateway(A, factors = c("walktrap", "louvain"), cent = c("strength","betweenness"))

Arguments



cent Centrality to community gateway coefficient. Defaults to "strength". Set to"betweenness" to use the betweenness centrality

hybrid 31

Value

Returns a list of overall (signs not considered; overall), negative (negative), and positive (positive)gateway coefficients

Author(s)


References


Vargas, E. R., & Wahl, L. M. (2014). The gateway coefficient: A novel metric for identifyingcritical connections in modular networks. The European Physical Journal B, 87(161), 1-10.

Examples



gw <- gateway(A, factors = factors)

#walktrap factorswgw <- gateway(A, factors = "walktrap")

hybrid Hybrid Centrality

Description

Computes hybrid centrality of each node in a network

Usage

hybrid(A, BC = c("standard", "random", "average"), beta)

Arguments


BC How should the betweenness centrality be computed? Defaults to "standard".Set to "random" for rspbc. Set to "average" for the average of "standard" and"random"

beta Beta parameter to be passed to the rspbc function

32 impact

Value

A vector of hybrid centrality values for each node in the network (higher values are more central,lower values are more peripheral)

Author(s)


References

Pozzi, F., Di Matteo, T., & Aste, T. (2013). Spread of risk across financial markets: Better to investin the peripheries. Scientific Reports, 3(1655), 1-7.

Examples

A<-TMFG(neoOpen)$A

HC<-hybrid(A)

impact Node Impact

Description

Computes impact measure or how much the average distance in the network changes with that noderemoved of each node in a network (Please see and cite Kenett et al., 2011. See also impact for aseparate but related measure)

Usage

impact(A, progBar = TRUE)

Arguments


progBar Defaults to FALSE. Set to TRUE to see progress bar

Value

A vector of node impact values for each node in the network (impact > 0, greater ASPL when nodeis removed; impact < 0, lower ASPL when node is removed)

Author(s)


is.graphical 33

References

Kenett, Y. N., Kenett, D. Y., Ben-Jacob, E., & Faust, M. (2011). Global and local features ofsemantic networks: Evidence from the Hebrew mental lexicon. PloS one, 6(8), e23912.

Examples

A<-TMFG(neoOpen)$A

nodeimpact<-impact(A)

is.graphical Determines if Network is Graphical

Description

Tests for whether the network is graphical. Input must be a partial correlation network. Functionassumes that partial correlations were computed from a multivariate normal distribution

Usage

is.graphical(A)

Arguments

A A partial correlation network (adjacency matrix)

Value

Returns a TRUE/FALSE for whether network is graphical

Author(s)


Examples

A <- LoGo(neoOpen, normal = TRUE, partial = TRUE)

is.graphical(A)

34 kld

kld Kullback-Leibler Divergence

Description

Estimates the Kullback-Leibler Divergence which measures how one probability distribution di-verges from the original distribution (equivalent means are assumed) Matrices must be positivedefinite inverse covariance matrix for accurate measurement. This is not a quantitative metric

Usage

kld(base, test)

Arguments

base Full or base model

test Reduced or testing model

Value

A value greater than 0. Smaller values suggest the probablity distribution of the reduced model isnear the full model

Author(s)


References

Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of MathematicalStatistics, 22(1), 79-86.

Examples

A1 <- solve(cov(neoOpen))

A2 <- LoGo(neoOpen)

kld_value <- kld(A1, A2)

lattnet 35

lattnet Generates a Lattice Network

Description

Generates a lattice network

Usage

lattnet(nodes, edges)

Arguments

nodes Number of nodes in lattice network

edges Number of edges in lattice network

Value

Returns an adjacency matrix of a lattice network

Author(s)


References


Examples

latt <- lattnet(10,27)

leverage Leverage Centrality

Description

Computes leverage centrlaity of each node in a network (the degree of connected neighbors) (Pleasesee and cite Joyce et al., 2010)

Usage

leverage(A, weighted = TRUE)

36 LoGo

Arguments


weighted Is the network weighted? Defaults to TRUE. Set to FALSE for unweightedmeasure of leverage centrality

Value

A vector of leverage centrality values for each node in the network

Author(s)


References

Joyce, K. E., Laurienti, P. J., Burdette, J. H., & Hayasaka, S. (2010). A new measure of centralityfor brain networks. PLoS One, 5(8), e12200.

Examples

A<-TMFG(neoOpen)$A

weighted_lev<-leverage(A)

unweighted_lev<-leverage(A, weighted=FALSE)

LoGo Local/Global Sparse Inverse Covariance Matrix

Description

Applies the Local/Global method to estimate the sparse inverse covariance matrix using a TMFG-filtered network (see and cite Barfuss et al., 2016)

Usage

LoGo(data, cliques, separators, partial = FALSE, normal = FALSE,standardize = FALSE, na.data = c("pairwise", "listwise", "fiml", "none"))

Arguments

data Must be a dataset

cliques Cliques defined in the network. Input can be a list or matrix

separators Separators defined in the network. Input can be a list or matrix

partial Should the output network’s connections be the partial correlation between twonodes given all other nodes? Defaults to FALSE, which returns a sparse inversecovariance matrix. Set to TRUE for a partial correlation matrix

louvain 37


standardize Should inverse covariance matrix be standardized? Defaults to FALSE. Set toTRUE for inverse correlation matrix


Value

Returns the sparse LoGo-filtered inverse covariance matrix (partial = FALSE) or LoGo-filteredpartial correlation matrix (partial = TRUE)

Author(s)


References

Barfuss, W., Massara, G. P., Di Matteo, T., & Aste, T. (2016). Parsimonious modeling with infor-mation filtering networks. Physical Review E, 94(6), 062306.

Examples

LoGonet<-LoGo(neoOpen, partial = TRUE)

louvain Louvain Community Detection Algorithm

Description

Computes a vector of communities (community) and a global modularity measure (Q)

Usage

louvain(A, gamma, M0)

Arguments


gamma Defaults to 1. Set to gamma > 1 to detect smaller modules and gamma < 1 forlarger modules

M0 Input can be an initial community vector. Defaults to none

38 MaST

Value

Returns a list of community and Q

Author(s)


References



Examples

A<-TMFG(neoOpen)$A

modularity<-louvain(A)

MaST Maximum Spanning Tree

Description

Applies the Maximum Spanning Tree (MaST) filtering method

Usage

MaST(data, normal = FALSE, weighted = TRUE, depend = FALSE,na.data = c("pairwise", "listwise", "fiml", "none"))

Arguments

data Can be a dataset or a correlation matrixnormal Should data be transformed to a normal distribution? Defaults to FALSE. Data

is not transformed to be normal. Set to TRUE if data should be transformed tobe normal (computes correlations using the cor_auto function)


depend Is network a dependency (or directed) network? Defaults to FALSE. Set TRUEto generate a MaST-filtered dependency network (output obtained from the dependfunction)


nams 39

Value


Author(s)


References

Adapted from: https://www.mathworks.com/matlabcentral/fileexchange/23276-maximum-weight-spanning-tree--undirected

Examples

weighted_MaSTnetwork<-MaST(neoOpen)

unweighted_MaSTnetwork<-MaST(neoOpen,weighted=FALSE)

nams Network Adjusted Mean/Sum

Description

The hybrid centrality is used to adjust the mean or sum score of participant’s factor scores basedon each node’s centrality. Each participant’s response values are multipled by the correspondinghybrid centrality value (uses "random" for BC argument). In this way, more central nodes contributea greater score and less central nodes contribute a lesser score

Usage

nams(data, A, normal = FALSE, standardize = TRUE, adjusted = c("mean","sum"), factors = c("walktrap", "louvain"), method = c("TMFG", "LoGo","EBICglasso", "IsingFit", "threshold"), na.data = c("pairwise", "listwise","fiml", "none"), ...)

Arguments


A Adjacency matrix that has already been filtered


standardize Should mean/sum scores be standardized? Defaults to TRUE. Set to FALSE forunstandardized mean/sum scores

adjusted Should adjusted values be the mean or sum score? Defaults to "mean". Set to"sum" for sum scores

https://www.mathworks.com/matlabcentral/fileexchange/23276-maximum-weight-spanning-tree--undirected

40 neoOpen

factors Can be a vector of factor assignments or community detection algorithms ("walk-trap" or "louvain") can be used to determine the number of factors. Defaults to 1factor. Set to "walktrap" for the walktrap algortihm. Set to "louvain" for louvaincommunity detection

method A network filtering method. Defaults to "TMFG". The "threshold" option is setto the default arguments (thresh = "alpha", a = .05). To use additional argumentsfor "threshold", apply threshold function with desired arguments and input intoargument A



Value

Returns a list of the network adjusted score (NetAdjScore) and the items associated with the speci-fied or identified factors (FactorItems)

Author(s)


Examples

sumadj <- nams(neoOpen, adjusted = "sum")

knownfactors <- nams(neoOpen,factors = c(rep(1,8),rep(2,8),rep(3,8),rep(4,8),rep(5,8),rep(6,8)))

walkadj <- nams(neoOpen, method="threshold", factors = "walktrap")

neoOpen NEO-PI-3 Openness to Experience Data

Description

A response matrix (n = 802) of NEO-PI-3’s Openness to Experience from Christensen, Cotter, &Silvia (in press).

Usage

data(neoOpen)

neuralcorrtest 41

Format

A 802x48 response matrix

References

Christensen, A.P., Cotter, K.N., & Silvia, P.J. (in press). Reopening Openness to Experience: Anetwork analysis of four Openness to Experience inventories. Journal of Personality Assessment,doi: 10.1080/00223891.2018.1467428 http://doi.org/10.17605/OSF.IO/954A7

Examples

data("neoOpen")

neuralcorrtest Neural-Behavioral Correlation Test

Description

Correlational test for global or local network characteristics of neural network data with behavioraldata (still in testing phase)

Usage

neuralcorrtest(bstat, nstat)

Arguments

bstat Behavioral statistic for each participant with neural data

nstat A statistic vector (whole-network) or matrix (ROI) from the neuralstat function

Value

Returns correlation test statistics for given statistics

Author(s)


Examples


filteredneuralarray <- neuralnetfilter(neuralarray, method = "threshold", thres = .50)

AverageShortestPathLength <- neuralstat(filteredneuralarray, statistic = "ASPL")

Degree <- neuralstat(filteredneuralarray, statistic = "deg")

http://doi.org/10.17605/OSF.IO/954A7

42 neuralgrouptest

bstat <- iq

WholeNetwork_corr <- neuralcorrtest(bstat, AverageShortestPathLength)

ROI_corr <- neuralcorrtest(bstat, Degree)

## End(Not run)

neuralgrouptest Neural Network Group Statistics Tests

Description

Statistical test for group differences for global or local network characteristics of neural networkdata (still in testing phase)

Usage

neuralgrouptest(groups, nstat, correction = c("bonferroni", "FDR"))

Arguments

groups Participant vector of desired groups (see examples)

nstat A statistic vector (whole-network) or matrix (ROI) from the neuralstat func-tion

correction Multiple comparisons correction for ROI testing. Defaults to local false discov-ery rate (i.e., "FDR")

Value

Returns test statistics for given groups and statistics

Author(s)


Examples





groups <- c(rep(1,30),rep(2,30))

neuralnetfilter 43

WholeNetwork_t-test <- neuralstattest(groups, AverageShortestPathLength)

ROI_t-test <- neuralstattest(groups, Degree)

## End(Not run)

neuralnetfilter Neural Network Filter

Description

Applies a network filtering methodology to neural network array. Removes edges from the neuralnetwork output from convertConnBrainMat using a network filtering approach

Usage

neuralnetfilter(neuralarray, method = c("TMFG", "MaST", "ECOplusMaST", "ECO","threshold"), progBar = TRUE, ...)

Arguments


method Filtering method to be applied


... Additional arguments from filtering methods

Value

Returns an array of n x n x m filtered matrices

Author(s)


References


Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulatedmaximally filtered graph. Journal of Complex Networks, 5(2), 161-178.

44 neuralstat

Examples

## Not run: neuralarray <- convertConnBrainMat()

filteredneuralarray <- neuralnetfilter(neuralarray, method = "threshold", thresh = .50)

dependencyarray <- depna(neuralarray)

filtereddependencyarray <- neuralnetfilter(dependencyarray, method = "TMFG", depend = TRUE)

## End(Not run)

neuralstat Local and Global Neural Network Characteristics

Description

Obtains a global or local network characteristic from neural network data

Usage

neuralstat(filarray, statistic = c("CC", "ASPL", "Q", "S", "transitivity","conn", "BC", "LC", "deg", "inDeg", "outDeg", "degRI", "str", "inStr","outStr", "strRI", "comm", "EC", "lev", "rspbc", "hybrid", "impact"),progBar = TRUE, ...)

Arguments

filarray Filtered array from neuralnetfilter function

statistic A statistic to compute


... Additional arguments for statistics functions

Value

Returns vector of global characteristics (rows = participants, columns = statistic) or a matrix of localcharacteristics (rows = ROIs, columns = participants)

Author(s)


neuralstat 45

References


Joyce, K. E., Laurienti, P. J., Burdette, J. H., & Hayasaka, S. (2010). A new measure of centralityfor brain networks. PLoS One, 5(8), e12200.

Kenett, Y. N., Kenett, D. Y., Ben-Jacob, E., & Faust, M. (2011). Global and local features ofsemantic networks: Evidence from the Hebrew mental lexicon. PloS one, 6(8), e23912.

Kivimaki, I., Lebichot, B., Saramaki, J., & Saerens, M. (2016). Two betweenness centrality mea-sures based on Randomized Shortest Paths. Scientific Reports, 6(19668), 1-15.

Pozzi, F., Di Matteo, T., & Aste, T. (2013). Spread of risk across financial markets: Better to investin the peripheries. Scientific Reports, 3(1655), 1-7.


Examples



ClusteringCoefficient <- neuralstat(filteredneuralarray, statistic = "CC")


Modularity <- neuralstat(filteredneuralarray, statistic = "Q")

Smallworldness <- neuralstat(filteredneuralarray, statistic = "S")

Trasitivity <- neuralstat(filteredneuralarray, statistic = "transitivity")

Connectivity <- neuralstat(filteredneuralarray, statistic = "conn")

BetweennessCentrality <- neuralstat(filteredneuralarray, statistic = "BC")

ClosenessCentrality <- neuralstat(filteredneuralarray, statistic = "LC")


NodeStrength <- neuralstat(filteredneuralarray, statistic = "str")

Communities <- neuralstat(filteredneuralarray, statistic = "comm")

EigenvectorCentrality <- neuralstat(filteredneuralarray, statistic = "EC")

LeverageCentrality <- neuralstat(filteredneuralarray, statistic = "lev")

RandomShortestPathBC <- neuralstat(filteredneuralarray, statistic = "rspbc")

46 participation

HybridCentrality <- neuralstat(filteredneuralarray, statistic = "hybrid")

NodeImpact <- neuralstat(filteredneuralarray, statistic = "impact")

## End(Not run)

participation Participation Coefficient

Description

Computes the participation coefficient for each node. The participation coefficient measures thestrength of a node’s connections within its community. Positive and negative signed weights forparticipation coefficients are computed separately.

Usage

participation(A, factors = c("walktrap", "louvain"))

Arguments



Value

Returns a list of overall (signs not considered; overall), negative (negative), and positive (positive)participation coefficient. Values closer to 1 suggest greater within-community connectivity andvalues closer to 0 suggest greater between-community connectivity

Author(s)


References

Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks.Nature, 433(7028), 895-900.


pathlengths 47

Examples



pc <- participation(A, factors = factors)

#walktrap factorswpc <- participation(A, factors = "walktrap")

pathlengths Characteristic Path Lengths

Description

Computes global average shortest path length (ASPL), local average shortest path length (ASPLi),eccentricity (ecc), and diameter (D) of a network

Usage

pathlengths(A, weighted = FALSE)

Arguments

A An adjacency matrix of network dataweighted Is the network weighted? Defaults to FALSE. Set to TRUE for weighted mea-

sures of ASPL, ASPLi, ecc, and D

Value

Returns a list of ASPL, ASPLi, ecc, and D of a network

Author(s)


References


Examples

A<-TMFG(neoOpen)$A

unweighted_PL<-pathlengths(A)

weighted_PL<-pathlengths(A, weighted=TRUE)

48 reg

randnet Generates a Random Network

Description

Generates a random network

Usage

randnet(nodes, edges)

Arguments

nodes Number of nodes in random network

edges Number of edges in random network

Value

Returns an adjacency matrix of a random network

Author(s)


References


Examples

rand <- randnet(10,27)

reg Regression Matrix

Description

Computes regression such that one variable is regressed over all other variables

Usage

reg(data, family = c("binomial", "gaussian", "Gamma", "poisson"),symmetric = TRUE)

rmse 49

Arguments

data A dataset

family Error distribution to be used in the regression model. Defaults to "logistic". Setto any family used in function family

symmetric Should matrix be symmetric? Defaults to TRUE, taking the mean of the twoedge weights (i.e., [i,j] and [j,i]) Set to FALSE for asymmwtric weights (i.e.,[i,j] does not equal [j,i])

Value

A matrix of fully regressed coefficients where one variable is regressed over all others

Author(s)


Examples

#binarize responsespsyb <- ifelse(neoOpen>=4, 1, 0)

#perform logistic regressionmat <- reg(psyb)

rmse Root Mean Square Error

Description

Computes the root mean square error of a sparse model to a full model

Usage

rmse(base, test)

Arguments

base Base (or full) model to be evaulated against

test Reduced (or testing) model (e.g., a sparse correlation or covariance matrix)

Value

RMSE value. Lower values suggest more similarity between the full and sparse model

Author(s)


50 rspbc

Examples

A1 <- solve(cov(neoOpen))

A2 <- LoGo(neoOpen)

root <- rmse(A1, A2)

rspbc Randomized Shortest Paths Betweenness Centrality

Description

Computes betweenness centrlaity based on randomized shortest paths of each node in a network(Please see and cite Kivimaki et al., 2016)

Usage

rspbc(A, beta = 0.01)

Arguments


beta Sets the beta parameter. Defaults to 0.01 (recommended). Beta > 0.01 measuregets closer to weighted betweenness centrality (10) and beta < 0.01 measure getscloser to degree (.0001)

Value

A vector of randomized shortest paths betweenness centrality values for each node in the network

Author(s)


References

Kivimaki, I., Lebichot, B., Saramaki, J., & Saerens, M. (2016). Two betweenness centrality mea-sures based on Randomized Shortest Paths. Scientific Reports, 6(19668), 1-15.

Examples

A<-TMFG(neoOpen)$A

rspbc<-rspbc(A, beta=0.01)

semnetboot 51

semnetboot Partial Bootstrapped Semantic Network Analysis

Description

Bootstraps (without replacement) the nodes in the network and computes global network character-istics

Usage

semnetboot(data, method = c("TMFG", "LoGo", "MaST", "threshold"),normal = FALSE, nodes, iter = 1000, na.data = c("pairwise", "listwise","fiml", "none"), ...)

Arguments

data A set of data



nodes Number of nodes (i.e., variables) to use in the bootstrap. Defaults to 50 Other-wise accepts the number of the nodes to be included



... Additional arguments for filtering methods

Value

Returns a list that includes the original semantic network measures (origmeas; ASPL, CC, Q, S),and the bootstrapped semantic network measures (bootmeas)

References

Kenett, Y. N., Wechsler-Kashi, D., Kenett, D. Y., Schwartz, R. G., Ben Jacob, E., & Faust, M.(2013). Semantic organization in children with cochlear implants: Computational analysis of verbalfluency. Frontiers in Psychology, 4(543), 1-11.

52 semnetmeas

Examples

## Not run:lowO <- subset(animals, Group==1)[-1]

semTMFG<-semnetboot(lowO)

semLoGo<-semnetboot(lowO,method="LoGo")

semMaST<-semnetboot(lowO,method="MaST")

semThreshold<-semnetboot(lowO,method="threshold")

## End(Not run)

semnetmeas Semantic Network Measures

Description

Computes the average shortest path length (ASPL), clustering coefficient(CC), modularity (Q), andsmall-worldness (S; defaults to "rand")

Usage

semnetmeas(A, iter)

Arguments


iter Number of iterations for the small-worldness measure

Value

Returns a values for ASPL, CC, Q, and S

Author(s)


Examples

## Not run:lowO <- subset(animals, Group==1)[-1]

A<-TMFG(lowO)

globmeas<-semnetmeas(A)

## End(Not run)

sim.swn 53

sim.swn Simulate Small-world Network

Description

Simulates a small-world network based on specified topological properties. Data will also be simu-lated based on the true network structure

Usage

sim.swn(nodes, n, pos = 0.8, ran = c(0.3, 0.7), nei = 1, p = 0.5,corr = FALSE, replace = NULL, ordinal = FALSE, ordLevels = NULL)

Arguments

nodes Number of nodes in the simulated network

n Number of cases in the simulated dataset

pos Proportion of positive correlations in the simulated network

ran Range of correlations in the simulated network

nei Adjusts the number of connections each node has to neighboring nodes (seesample_smallworld)

p Adjusts the rewiring probability (default is .5). p > .5 rewires the simulatednetwork closer to a random network. p < .5 rewires the simulated network closerto a lattice network

corr Should the simualted network be a correlation network? Defaults to FALSE. Setto TRUE for a simulated correlation network

replace If noise > 0, then should participants be sampled with replacement? Defaults toTRUE. Set to FALSE to not allow the potential for participants to be consecu-tively entered into the simulated dataset.

ordinal Should simulated continuous data be converted to ordinal? Defaults to FALSE.Set to TRUE for simulated ordinal data

ordLevels If ordinal = TRUE, then how many levels should be used? Defaults to NULL.Set to desired number of intervals (defaults to 5)

Value

Returns a list that includes the simulated network (simNetwork), simulated data (simData), andsimulated correlation matrix (simRho)

Author(s)


54 smallworldness

References

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research.InterJournal, Complex Systems, 1695(5), 1-9.

Examples

sim.norm <- sim.swn(25, 500, nei = 3)

sim.Likert <- sim.swn(25, 500, nei = 3,replace = TRUE, ordinal = TRUE, ordLevels = 5)

smallworldness Small-worldness Measure

Description

Computes the small-worldness measure of a network

Usage

smallworldness(A, iter = 100, progBar = FALSE, method = c("HG", "rand","TJHBL"))

Arguments


iter Number of random (or lattice) networks to generate, which are used to calculatethe mean random ASPL and CC (or lattice)

progBar Defaults to FALSE. Set to TRUE to see progress bar

method Defaults to "HG" (Humphries & Gurney, 2008). Set to "rand" for the CC to becalculated using a random network or set to "TJHBL" for (Telesford et al., 2011)where CC is calculated from a lattice network

Value

Returns a list with value of small-worldness (swm) and random ASPL (rASPL). For "rand", values> 1 indicate a small-world network (lrCCt = random CC). For "HG", values > 3 indicate a small-world network (lrCCt = random Transitivity). For "TJHBL" values near 0 indicate a small-worldnetwork (lrCCt = lattice CC), while < 0 indicates a more regular network and > 0 indicates a morerandom network

Author(s)


splitsamp 55

References

Humphries, M. D., & Gurney, K. (2008). Network ’small-world-ness’: A quantitative method fordetermining canonical network equivalence. PloS one, 3(4), e0002051.

Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., & Laurienti, P. J. (2011). The ubiquityof small-world networks. Brain Connectivity, 1(5), 367-375.

Examples

A<-TMFG(neoOpen)$A

swmHG <- smallworldness(A, method="HG")

swmRand <- smallworldness(A, method="rand")

swmTJHBL <- smallworldness(A, method="TJHBL")

splitsamp Split sample

Description

Randomly splits sample into equally divded sub-samples

Usage

splitsamp(data, samples = 10, splits = 2)

Arguments


samples Number of samples to produce (Defaults to 10)

splits Number to divide the sample by (Defaults to 2; i.e., split-half)

Value

A list containing the training (trainSample) and testing (testSample) sizes (training sample is madeto have slightly larger sizes in the case of uneven splits), and their respective sample sizes (trainSizeand testSize)

Author(s)


Examples

splithalf<-splitsamp(neoOpen)

56 splitsampNet

splitsampNet Network Construction for splitsamp

Description

Applies a network construction method to samples generated from the splitsamp function

Usage

splitsampNet(object, method = c("MaST", "TMFG", "LoGo", "threshold"),bootmat = FALSE, pB = TRUE, ...)

Arguments

object Object output from splitsamp function


bootmat Should bootgen function be used? Defaults to FALSE. Set to TRUE to constuc-tion bootgen networks

pB Should progress bar be displayed? Defaults to TRUE. Set to FALSE for noprogress bar

... Additional arguments to be passed to the network construction methods

Value

Returns a list of networks for training (train) and testing (test) samples

Author(s)


Examples

samples <- splitsamp(neoOpen)

nets <- splitsampNet(samples, method="TMFG")

splitsampStats 57

splitsampStats Statistics for splitsamp Networks

Description

Applies a network statistics to the networks generated from the splitsampNet function

Usage

splitsampStats(object, stats = c("edges", "centralities"),edges = c("lower", "upper"), BC = c("standard", "random"))

Arguments

object Object output from splitsampNet function

stats Which stats should be computed? "edges" will compute statistics for the edgerepfunction. "centralities" will compute betweenness, closeness, and strength. Bothoptions are allowed

edges If stats = "edges, should the lower or upper replication percentage be used?Defaults to "lower".

BC How should the betweenness centrality be computed? Defaults to "standard"betweenness centrlaity. Set to "random" for rspbc

Value

Returns a list of statistics for edges (Edges), centralities (Centralities), or both

Author(s)


Examples

samples <- splitsamp(neoOpen)

nets <- splitsampNet(samples, method="TMFG")

stats <- splitsampStats(nets, stats = "edges", edges = "lower")

58 stable

stable Stabilizing Nodes

Description

Computes the within-community centrality for each node in the network

Usage

stable(A, factors = c("walktrap", "louvain"), cent = c("betweenness","rspbc", "strength", "degree", "hybrid"), ...)

Arguments


factors Can be a vector of factor assignments or community detection algorithms ("walk-trap" or "louvain") can be used to determine the number of factors. Defaults to"walktrap". Set to "louvain" for louvain community detection

cent Centrality measure to be used. Defaults to "strength".


Value

A matrix containing the within-community centrality value for each node

Author(s)


References

Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., Kerkhof, G. A., & Cramer,A. O. (2018). The role of stabilizing and communicating symptoms given overlapping communitiesin psychopathology networks. Scientific Reports, 8(1), 5854.

Examples

A<-TMFG(neoOpen)$A

stabilizing <- stable(A, factors = "walktrap")

strength 59

strength Node Strength

Description

Computes strength of each node in a network

Usage

strength(A)

Arguments


Value

A vector of strength values for each node in the network. If directed network, returns a list ofin-strength (inStrength), out-strength (outStrength), and relative influence (relInf)

Author(s)


References


Examples

A<-TMFG(neoOpen)$A

str<-strength(A)

threshold Threshold Filter

Description

Filters the network based on an r-value, alpha, adaptive alpha (see Perez & Pericchi, 2014), bonfer-roni, false-discovery rate (FDR, fdrtool package), or proportional density (fixed number of edges)value

60 threshold

Usage

threshold(data, a, thresh = c("alpha", "adaptive", "bonferroni", "FDR","proportional"), n = nrow(data), normal = FALSE, na.data = c("pairwise","listwise", "fiml", "none"))

Arguments


a When thresh = "alpha", "adaptive", and "bonferroni" an alpha threshold is ap-plied (defaults to .05). For "adaptive", beta (Type II error) is set to a*5 for amedium effect size (r = .3). When thresh = "FDR", a q-value threshold is ap-plied (defaults to .10). When thresh = "proportional", a density threshold isapplied (defaults to .15)

thresh Sets threshold. Defaults to "alpha". Set to any value 0> r >1 to retain valuesgreater than set value, "adaptive" for an adapative alpha based on sample size(Perez & Pericchi, 2014), "bonferroni" for the bonferroni correction on alpha,"FDR" for local false discovery rate, and "proportional" for a fixed density ofedges (keeps strongest correlations within density)

n Number of participants in sample. Defaults to the number of rows in the data. Ifinput is a correlation matrix, then n must be set



Value

Returns a list containing a filtered adjacency matrix (A) and the critical r value (r.cv)

Author(s)


References

Perez, M. E., & Pericchi, L. R. (2014). Changing statistical significance with the amount of infor-mation: The adaptive a significance level. Statistics & Probability Letters, 85, 20-24.

Strimmer, K. (2008). fdrtool: A versatile R package for estimating local and tail area-based falsediscovery rates. Bioinformatics, 24(12), 1461-1462.

TMFG 61

Examples

threshnet<-threshold(neoOpen)

alphanet<-threshold(neoOpen, thresh = "alpha", a = .05)

bonnet<-threshold(neoOpen, thresh = "bonferroni", a = .05)

FDRnet<-threshold(neoOpen, thresh = "FDR", a = .10)

propnet<-threshold(neoOpen, thresh = "proportional", a = .15)

TMFG Triangulated Maximally Filtered Graph

Description

Applies the Triangulated Maximally Filtered Graph (TMFG) filtering method (Please see and citeMassara et al., 2016)

Usage

TMFG(data, normal = FALSE, weighted = TRUE, depend = FALSE,na.data = c("pairwise", "listwise", "fiml", "none"))

Arguments


normal Should data be transformed to a normal distribution? Input must be a dataset.Defaults to FALSE. Data is not transformed to be normal. Set to TRUE if datashould be transformed to be normal (computes correlations using the cor_autofunction)


depend Is network a dependency (or directed) network? Defaults to FALSE. Set toTRUE to generate a TMFG-filtered dependency network (output obtained fromthe depend function)


Value

Returns a list of the adjacency matrix (A), separators (separators), and cliques (cliques)

62 transitivity

Author(s)


References

Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018). Network struc-ture of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filteringapproaches. Behavior Research Methods, 1-20, doi:10.3758/s13428-018-1032-9

Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulatedmaximally filtered graph. Journal of Complex Networks, 5(2), 161-178.

Examples

weighted_TMFGnetwork<-TMFG(neoOpen)

unweighted_TMFGnetwork<-TMFG(neoOpen,weighted=FALSE)

transitivity Transitivity

Description

Computes transitivity of a network

Usage

transitivity(A, weighted = FALSE)

Arguments


weighted Is the network weighted? Defaults to FALSE. Set to TRUE for a weighted mea-sure of transitivity

Value

Returns a value of transitivity

Author(s)


References


transitivity 63

Examples

A<-TMFG(neoOpen)$A

trans<-transitivity(A, weighted=TRUE)

Index

∗Topic datasetsanimals, 4behavOpen, 4neoOpen, 40

animals, 4

behavOpen, 4betweenness, 5binarize, 6bootgen, 7, 56bootgenPlot, 8

centlist, 9closeness, 10clustcoeff, 11comcat, 12commboot, 13conn, 14convert2igraph, 15convertConnBrainMat, 15, 16, 18–21, 24, 43cor2cov, 17cor_auto, 7, 23, 37–39, 51, 60, 61corFiml, 7, 13, 23, 37, 38, 40, 51, 60, 61cpmEV, 17cpmFP, 18cpmFPperm, 19cpmIV, 20

degree, 22depend, 22, 24, 38, 61depna, 24distance, 25diversity, 26

EBICglasso, 13ECO, 27ECOplusMaST, 28edgerep, 28, 57eigenvector, 29

family, 49

gateway, 30

hybrid, 31

igraph, 15impact, 32, 32is.graphical, 33IsingFit, 13

kld, 34

lattnet, 35leverage, 35LoGo, 36louvain, 37

MaST, 38

na.omit, 7, 13, 23, 37, 38, 40, 51, 60, 61nams, 39neoOpen, 40NetworkToolbox

(NetworkToolbox-package), 3NetworkToolbox-package, 3neuralcorrtest, 41neuralgrouptest, 42neuralnetfilter, 43, 44neuralstat, 42, 44

participation, 46pathlengths, 47

randnet, 48reg, 48rmse, 49rspbc, 50, 57

sample_smallworld, 53semnetboot, 51

64

INDEX 65

semnetmeas, 52sim.swn, 53smallworldness, 54splitsamp, 55, 56splitsampNet, 56, 57splitsampStats, 57stable, 58strength, 59

threshold, 59TMFG, 61transitivity, 62

package ‘networktoolbox’ · package ‘networktoolbox ... neo-pi-3 openness to experience...

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