| Title: | Binary Graph Analysis Tools |
|---|---|
| Description: | Tools to analyze binary graph objects. |
| Authors: | Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon |
| Maintainer: | Berkley Shands <[email protected]> |
| License: | Apache License (== 2.0) |
| Version: | 1.3 |
| Built: | 2024-12-01 08:16:06 UTC |
| Source: | CRAN |
Tools for analyzing binary graphs, including calculating the MLE of a set of binary graphs, comparing MLE of sets of graphs, regression analysis on sets of graphs, using genetic algorithm to identify nodes and edges separating sets of graphs, and generating random binary graphs sampled from the Gibbs distribution.
The following are the types of binary graphs that are accepted:
adjMatrix: An entire binary adjacency matrix as a single vector
adjMatrixLT: The upper or lower triangle of a binary adjacency matrix as a single vector
diag: The diagonal vector on a binary adjacency matrix
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
Stat Med. 2015 Nov 25. doi: 10.1002/sim.6757. Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images. La Rosa PS1,2, Brooks TL1, Deych E1, Shands B1,3, Prior F4, Larson-Prior LJ4,5, Shannon WD1,3.
A data set containing 38 brain scans each with 20 total nodes.
data(braingraphs)data(braingraphs)
The format is a data frame of 400 rows by 38 columns, with each column being a separate subject and each row being a different edge between 2 nodes. Each column is a 20 by 20 matrix of brain connections transformed into a vector. A value of 1 indicates that subject had a connection at that edge.
This function calculates the distance between two vectors.
calcDistance(x, y, type = "", method = "hamming")calcDistance(x, y, type = "", method = "hamming")
x, y
|
Vectors of the same length that contain 1's and 0's. |
type |
The type of graph being used (adjmatrix or adjmatrixlt). See 'Details' |
method |
The distance metric to use, currently only "hamming" is supported. |
If the type = "adjMatrix" is used, the value will be divided by 2 to account for duplicate comparisons.
Otherwise the type does not affect the output.
A single number indicating the distance between the two input vectors.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) dist <- calcDistance(braingraphs[,1], braingraphs[,2], "adjMatrix") distdata(braingraphs) dist <- calcDistance(braingraphs[,1], braingraphs[,2], "adjMatrix") dist
This function estimates the g-star graph for a given set of graphs.
estGStar(data)estGStar(data)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
A single vector that is the gstar is returned.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) braingstar <- estGStar(braingraphs) braingstar[1:25]data(braingraphs) braingstar <- estGStar(braingraphs) braingstar[1:25]
This function estimates log likelihood value for a given graph.
estLogLik(data, type, gstar, tau, g = NULL)estLogLik(data, type, gstar, tau, g = NULL)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
gstar |
A single vector to estimate the likelihood for. |
tau |
A single value used in estimating the likelihood. |
g |
Deprecated. Replaced with gstar for clarity. |
The log-likelihood value of the data.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) braingstar <- estGStar(braingraphs) braintau <- estTau(braingraphs, "adjMatrix", braingstar) brainll <- estLogLik(braingraphs, "adjMatrix", braingstar, braintau) brainlldata(braingraphs) braingstar <- estGStar(braingraphs) braintau <- estTau(braingraphs, "adjMatrix", braingstar) brainll <- estLogLik(braingraphs, "adjMatrix", braingstar, braintau) brainll
This function estimates the MLE parameters g-star and tau for a given set of graphs.
estMLE(data, type)estMLE(data, type)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
Essentially this function calls both estGStar and estTau and returns the results.
A list containing g-star and tau named gstar and tau respectively.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) brainmle <- estMLE(braingraphs, "adjMatrix") brainmledata(braingraphs) brainmle <- estMLE(braingraphs, "adjMatrix") brainmle
This function estimates tau for a given set of graphs.
estTau(data, type, gstar)estTau(data, type, gstar)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
gstar |
A single columned data frame to be used as the g-star of the data set. |
The tau value for the data based on g star.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) braingstar <- estGStar(braingraphs) braintau <- estTau(braingraphs, "adjMatrix", braingstar) braintaudata(braingraphs) braingstar <- estGStar(braingraphs) braintau <- estTau(braingraphs, "adjMatrix", braingstar) braintau
GA-Mantel is a fully multivariate method that uses a genetic algorithm to search over possible edge subsets using the Mantel correlation as the scoring measure for assessing the quality of any given edge subset.
genAlg(data, covars, iters = 50, popSize = 200, earlyStop = 0, dataDist = "manhattan", covarDist = "gower", verbose = FALSE, plot = TRUE, minSolLen = NULL, maxSolLen = NULL)genAlg(data, covars, iters = 50, popSize = 200, earlyStop = 0, dataDist = "manhattan", covarDist = "gower", verbose = FALSE, plot = TRUE, minSolLen = NULL, maxSolLen = NULL)
data |
A matrix of edges(rows) for each sample(columns). |
covars |
A matrix of covariates(columns) for each sample(rows). |
iters |
The number of times to run through the GA. |
popSize |
The number of solutions to test on each iteration. |
earlyStop |
The number of consecutive iterations without finding a better solution before stopping regardless of the number of iterations remaining. A value of '0' will prevent early stopping. |
dataDist |
The distance metric to use for the data. This can only be "manhattan" for now. |
covarDist |
The distance metric to use for the covariates. Either "euclidean" or "gower". |
verbose |
While 'TRUE' the current status of the GA will be printed periodically. |
plot |
A boolean to plot the progress of the scoring statistics by iteration. |
minSolLen |
The minimum number of columns to select. |
maxSolLen |
The maximum number of columns to select. |
Use a GA approach to find edges that separate subjects based on group membership or set of covariates.
The data and covariates should be normalized BEFORE use with this function because of distance functions.
This function uses modified code from the rbga function in the genalg package. rbga
Because the GA looks at combinations and uses the raw data, edges with a small difference may be selected and large differences may not be.
The distance calculations use the vegdist package. vegdist
A list containing
scoreSumm |
A matrix summarizing the score of the population. This can be used to figure out if the ga has come to a final solution or not. This data is also plotted if plot is 'TRUE'. |
solutions |
The final set of solutions, sorted with the highest scoring first. |
scores |
The scores for the final set of solutions. |
time |
How long in seconds the ga took to run. |
selected |
The selected edges by name. |
nonSelected |
The edges that were NOT selected by name. |
selectedIndex |
The selected edges by row number. |
Sharina Carter, Elena Deych, Berkley Shands, William D. Shannon
## Not run: data(braingraphs) ### Set covars to just be group membership covars <- matrix(c(rep(0, 19), rep(1, 19))) ### We use low numbers for speed. The exact numbers to use depend ### on the data being used, but generally the higher iters and popSize ### the longer it will take to run. earlyStop is then used to stop the ### run early if the results aren't improving. iters <- 500 popSize <- 200 earlyStop <- 250 gaRes <- genAlg(braingraphs, covars, iters, popSize, earlyStop) ## End(Not run)## Not run: data(braingraphs) ### Set covars to just be group membership covars <- matrix(c(rep(0, 19), rep(1, 19))) ### We use low numbers for speed. The exact numbers to use depend ### on the data being used, but generally the higher iters and popSize ### the longer it will take to run. earlyStop is then used to stop the ### run early if the results aren't improving. iters <- 500 popSize <- 200 earlyStop <- 250 gaRes <- genAlg(braingraphs, covars, iters, popSize, earlyStop) ## End(Not run)
GA-Mantel is a fully multivariate method that uses a genetic algorithm to search over possible edge subsets using the Mantel correlation as the scoring measure for assessing the quality of any given edge subset.
genAlgConsensus(data, covars, consensus = .5, numRuns = 10, parallel = FALSE, cores = 3, ...)genAlgConsensus(data, covars, consensus = .5, numRuns = 10, parallel = FALSE, cores = 3, ...)
data |
A matrix of edges(rows) for each sample(columns). |
covars |
A matrix of covariates(columns) for each sample(rows). |
consensus |
The required fraction (0, 1] of solutions containing an edge in order to keep it. |
numRuns |
Number of runs to do. In practice the number of runs needed varies based on data set size and the GA parameters set. |
parallel |
When this is 'TRUE' it allows for parallel calculation of the bootstraps. Requires the package |
cores |
The number of parallel processes to run if parallel is 'TRUE'. |
... |
Other arguments for the GA function see genAlg |
Use a GA consensus approach to find edges that separate subjects based on group membership or set of covariates if you cannot run the GA long enough to get a final solution.
A list containing
solutions |
The best solution from each run. |
consSol |
The consensus solution. |
selectedIndex |
The selected edges by row number. |
Sharina Carter, Elena Deych, Berkley Shands, William D. Shannon
## Not run: data(braingraphs) ### Set covars to just be group membership covars <- matrix(c(rep(0, 19), rep(1, 19))) ### We use low numbers for speed. The exact numbers to use depend ### on the data being used, but generally the higher iters and popSize ### the longer it will take to run. earlyStop is then used to stop the ### run early if the results aren't improving. iters <- 500 popSize <- 200 earlyStop <- 250 numRuns <- 3 gaRes <- genAlgConsensus(braingraphs, covars, .5, numRuns, FALSE, 3, iters, popSize, earlyStop) ## End(Not run)## Not run: data(braingraphs) ### Set covars to just be group membership covars <- matrix(c(rep(0, 19), rep(1, 19))) ### We use low numbers for speed. The exact numbers to use depend ### on the data being used, but generally the higher iters and popSize ### the longer it will take to run. earlyStop is then used to stop the ### run early if the results aren't improving. iters <- 500 popSize <- 200 earlyStop <- 250 numRuns <- 3 gaRes <- genAlgConsensus(braingraphs, covars, .5, numRuns, FALSE, 3, iters, popSize, earlyStop) ## End(Not run)
This function splits the data into groups based on the Gibbs criteria.
getGibbsMixture(data, type, desiredGroups, maxIter = 50, digits = 3)getGibbsMixture(data, type, desiredGroups, maxIter = 50, digits = 3)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
desiredGroups |
The number of groups to test for. |
maxIter |
The maximum number of iterations to run searching for an optimal split. |
digits |
The number of digits to round internal values to when checking the stop criteria. |
Generally this function is not used by itself but in conjunction with getLoglikeMixture.
A list that contains information about the group splits. The list contains the final weights, gstars and taus for every group, a boolean indicating convergence, the number of iterations it took, and the group for each graph.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) braingm <- getGibbsMixture(braingraphs, "adjMatrix", 5)data(braingraphs) braingm <- getGibbsMixture(braingraphs, "adjMatrix", 5)
This function takes group splits and determines the likelihood of those groups.
getLoglikeMixture(data, mixture, numConst)getLoglikeMixture(data, mixture, numConst)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
mixture |
The output of the |
numConst |
The numeric constant to multiply the loglikihood by. |
A list containing the BIC criteria and the log likelihood named bic and ll respectively.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) braingm <- getGibbsMixture(braingraphs, "adjMatrix", 5) brainlm <- getLoglikeMixture(braingraphs, braingm) brainlm ### By running the loglik mixture over several groups you can find which is the optimal ## Not run: mixtures <- NULL for(i in 1:5){ tempgm <- getGibbsMixture(braingraphs, "adjMatrix", i) mixtures[i] <- getLoglikeMixture(braingraphs, tempgm)$bic } bestgroupnum <- which(min(mixtures) == mixtures) bestgroupnum ## End(Not run)data(braingraphs) braingm <- getGibbsMixture(braingraphs, "adjMatrix", 5) brainlm <- getLoglikeMixture(braingraphs, braingm) brainlm ### By running the loglik mixture over several groups you can find which is the optimal ## Not run: mixtures <- NULL for(i in 1:5){ tempgm <- getGibbsMixture(braingraphs, "adjMatrix", i) mixtures[i] <- getLoglikeMixture(braingraphs, tempgm)$bic } bestgroupnum <- which(min(mixtures) == mixtures) bestgroupnum ## End(Not run)
This function will return the number of edges for a given of graph.
getNumEdges(nodes, type)getNumEdges(nodes, type)
nodes |
The number of individual nodes in a given graph. |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
The number of edges between individual nodes in the given graph.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) brainnodes <- getNumNodes(braingraphs, "adjMatrix") brainedges <- getNumEdges(brainnodes, "adjMatrix") brainedgesdata(braingraphs) brainnodes <- getNumNodes(braingraphs, "adjMatrix") brainedges <- getNumEdges(brainnodes, "adjMatrix") brainedges
This function will return the number of nodes for a given of graph.
getNumNodes(data, type)getNumNodes(data, type)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
The number of individual nodes in the given graph.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) brainnodes <- getNumNodes(braingraphs, "adjMatrix") brainnodesdata(braingraphs) brainnodes <- getNumNodes(braingraphs, "adjMatrix") brainnodes
This function returns the p-value of the significance of b1 in the regression model.
glrtPvalue(dataList, type, groups, numPerms = 10, parallel = FALSE, cores = 3, data = NULL)glrtPvalue(dataList, type, groups, numPerms = 10, parallel = FALSE, cores = 3, data = NULL)
dataList |
A list where each element is a data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
groups |
Deprecated. Each data set should be an element in dataList. |
numPerms |
Number of permutations. In practice this should be at least 1,000. |
parallel |
TRUE or FALSE depending on whether the analysis will be parallelized for speed. |
cores |
The number of cores to use for parallelization. Ignored if parallel = FALSE. |
data |
Deprecated. Replaced with dataList for clarity. |
A list containing the p-value, tau, logliklihood value, glrt value, bob1, b0, b1 and the hamming errors.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### We use 1 for speed, should be at least 1,000 numPerms <- 1 res <- glrtPvalue(dataList, "adjMatrix", numPerms=numPerms) res$pvaluedata(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### We use 1 for speed, should be at least 1,000 numPerms <- 1 res <- glrtPvalue(dataList, "adjMatrix", numPerms=numPerms) res$pvalue
This function plots the connections between nodes in a single subject.
graphNetworkPlot(data, type, main = "Network Plot", labels, groupCounts, groupLabels)graphNetworkPlot(data, type, main = "Network Plot", labels, groupCounts, groupLabels)
data |
A vector of a single graph. |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
main |
The title for the plot. |
labels |
A vector which contains the names for each node. |
groupCounts |
A vector which contains the number of nodes in each group of nodes. |
groupLabels |
A vector which contains the names for each group of nodes. |
A plot displaying the connections between the nodes.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) main <- "Brain Connections" gc <- c(5, 5, 4, 6) gl <- c("Grp1", "Grp2", "Grp3", "Grp4") graphNetworkPlot(braingraphs[,1], "adjMatrix", main, groupCounts=gc, groupLabels=gl)data(braingraphs) main <- "Brain Connections" gc <- c(5, 5, 4, 6) gl <- c("Grp1", "Grp2", "Grp3", "Grp4") graphNetworkPlot(braingraphs[,1], "adjMatrix", main, groupCounts=gc, groupLabels=gl)
This function returns the p-value of the significance between two groups.
lrtPvalue(dataList, type, groups, numPerms = 10, parallel = FALSE, cores = 3, data = NULL)lrtPvalue(dataList, type, groups, numPerms = 10, parallel = FALSE, cores = 3, data = NULL)
dataList |
A list where each element is a data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
groups |
Deprecated. Each data set should be an element in dataList. |
numPerms |
Number of permutations. In practice this should be at least 1,000. |
parallel |
TRUE or FALSE depending on whether the analysis will be parallelized for speed. |
cores |
The number of cores to use for parallelization. Ignored if parallel = FALSE. |
data |
Deprecated. Replaced with dataList for clarity. |
The p-value for the difference between the two groups being tested.
Berkley Shands, Elena Deych, William D. Shannon
data(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### We use 1 for speed, should be at least 1,000 numPerms <- 1 lrt <- lrtPvalue(dataList, "adjMatrix", numPerms=numPerms) lrtdata(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### We use 1 for speed, should be at least 1,000 numPerms <- 1 lrt <- lrtPvalue(dataList, "adjMatrix", numPerms=numPerms) lrt
This function returns the p-value of the significance of the difference in g-star values for paired data.
pairedPvalue(dataList, type, groups, numPerms = 10, parallel = FALSE, cores = 3, data = NULL)pairedPvalue(dataList, type, groups, numPerms = 10, parallel = FALSE, cores = 3, data = NULL)
dataList |
A list where each element is a data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
groups |
Deprecated. Each data set should be an element in dataList. |
numPerms |
Number of permutations. In practice this should be at least 1,000. |
parallel |
TRUE or FALSE depending on whether the analysis will be parallelized for speed. |
cores |
The number of cores to use for parallelization. Ignored if parallel = FALSE. |
data |
Deprecated. Replaced with dataList for clarity. |
The p-value for the difference between the two groups being tested.
Berkley Shands, Elena Deych, William D. Shannon
data(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### We use 1 for speed, should be at least 1,000 numPerms <- 1 pval <- pairedPvalue(dataList, "adjMatrix", numPerms=numPerms) pvaldata(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### We use 1 for speed, should be at least 1,000 numPerms <- 1 pval <- pairedPvalue(dataList, "adjMatrix", numPerms=numPerms) pval
This function plots the connections between nodes in a single subject as a heat map.
plotHeatmap(data, type, names, ...)plotHeatmap(data, type, names, ...)
data |
A vector of a single graph. |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
names |
A vector of names for labeling the nodes on the plot. |
... |
Arguments to be passed to the plot method. |
A plot displaying the connections between the nodes as a heat map.
Berkley Shands, Elena Deych, William D. Shannon
data(braingraphs) braingstar <- estGStar(braingraphs) plotHeatmap(braingstar, "adjMatrix")data(braingraphs) braingstar <- estGStar(braingraphs) plotHeatmap(braingstar, "adjMatrix")
This function plots all the data on an MDS plot.
plotMDS(dataList, groups, estGstar = TRUE, paired = FALSE, returnCoords = FALSE, ..., data=NULL)plotMDS(dataList, groups, estGstar = TRUE, paired = FALSE, returnCoords = FALSE, ..., data=NULL)
dataList |
A list where each element is a data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
groups |
Deprecated. Each data set should be an element in dataList. |
estGstar |
When TRUE, the g star for every group is calculated and plotted. |
paired |
When TRUE, line segments between pairs will be drawn. |
returnCoords |
When TRUE, the MDS x-y coordinates will be returned. |
... |
Arguments to be passed to the plot method. |
data |
Deprecated. Replaced with dataList for clarity. |
An MDS plot and if returnCoords is TRUE, a 2 column data frame containing the x-y coordinates of the data points is also returned.
Berkley Shands, Elena Deych, William D. Shannon
data(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### Basic plot plotMDS(dataList, main="MDS Plot") ### Paired Plot plotMDS(dataList, paired=TRUE, main="Paired MDS Plot")data(braingraphs) ### Break our data into two groups dataList <- list(braingraphs[,1:19], braingraphs[,20:38]) ### Basic plot plotMDS(dataList, main="MDS Plot") ### Paired Plot plotMDS(dataList, paired=TRUE, main="Paired MDS Plot")
Generate random data sampled from the Gibbs distribution.
rGibbs(gstar, tau, type, numGraphs = 1)rGibbs(gstar, tau, type, numGraphs = 1)
gstar |
G star vector. |
tau |
A single value that affects the dispersion of the generated data. |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
numGraphs |
The number of graphs to generate. |
A data frame containing all the graphs generated.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) braingstar <- estGStar(braingraphs) braintau <- estTau(braingraphs, "adjMatrix", braingstar) randombraingraphs <- rGibbs(braingstar, braintau, "adjMatrix", 3) randombraingraphs[1:5,]data(braingraphs) braingstar <- estGStar(braingraphs) braintau <- estTau(braingraphs, "adjMatrix", braingstar) randombraingraphs <- rGibbs(braingstar, braintau, "adjMatrix", 3) randombraingraphs[1:5,]
This function tests the goodness of fit for given a set of graphs.
testGoF(data, type, numSims = 10, plot = TRUE, main)testGoF(data, type, numSims = 10, plot = TRUE, main)
data |
A data frame in which the columns (subjects) contain a 0/1 value for row (Node or Edge). |
type |
The type of graph being used (adjmatrix or adjmatrixlt). |
numSims |
Number of simulations for Monte Carlo estimation of p-value(ideally, 1000 or more). Ignored if Chi-Square method is used. |
plot |
A boolean to create a plot of the results or not. |
main |
A title for the plot. |
A list containing information about the goodness of fit and potentially a plot. The list contains the Pearson statistics, degrees of freedom, and p-value, the G statistics and p-value, the Chi Squared statistics and p-value and finally the table with the observed and expected counts.
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
data(braingraphs) numSims <- 1 ### This is set low for speed braingof <- testGoF(braingraphs, "adjMatrix", numSims)data(braingraphs) numSims <- 1 ### This is set low for speed braingof <- testGoF(braingraphs, "adjMatrix", numSims)