Dynamic Graphical Models

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Documentation for package ‘DGM’ version 1.7.4

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binom.nettest Performes a binomial test with FDR correction for network edge occurrence.
center Mean centers timeseries in a 2D array timeseries x nodes, i.e. each timeseries of each node has mean of zero.
cor2adj Threshold correlation matrix to match a given number of edges.
corTs Mean correlation of time series across subjects.
dgm.group A group is a list containing restructured data from subejcts for easier group analysis.
diag.delta Quick diagnostics on delta.
dlm.lpl Calculate the log predictive likelihood for a specified set of parents and a fixed delta.
dlm.retro Calculate the location and scale parameters for the time-varying coefficients given all the observations. West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York.
dlmLplCpp C++ implementation of the dlm.lpl
exhaustive.search A function for an exhaustive search, calculates the optimum value of the discount factor.
getAdjacency Get adjacency and associated likelihoods (LPL) and disount factros (df) of winning models.
getIncompleteNodes Checks results and returns job number for incomplete nodes.
getModel Extract specific parent model with assocated df and ME from complete model space.
getModelNr Get model number from a set of parents.
getWinner Get winner network by maximazing log predictive likelihood (LPL) from a set of models.
gplotMat Plots network as adjacency matrix.
mergeModels Merges forward and backward model store.
model.generator A function to generate all the possible models.
myts Network simulation data.
node Runs exhaustive search on a single node and saves results in txt file.
patel Patel.
patel.group A group is a list containing restructured data from subejcts for easier group analysis.
perf Performance of estimates, such as sensitivity, specificity, and more.
priors.spec Specify the priors. Without inputs, defaults will be used.
prop.nettest Comparing two population proportions on the network with FDR correction.
pruning Get pruned adjacency network.
rand.test Randomization test for Patel's kappa. Creates a distribution of values kappa under the null hypothesis.
read.subject Reads single subject's network from txt files.
reshapeTs Reshapes a 2D concatenated time series into 3D according to no. of subjects and volumes.
rmdiag Removes diagonal of NA's from matrix.
rmna Removes NAs from matrix.
rmRecipLow Removes reciprocal connections in the lower diagnoal of the network matrix.
scaleTs Scaling data. Zero centers and scales the nodes (SD=1).
stepwise.backward Stepise backward non-exhaustive greedy search, calculates the optimum value of the discount factor.
stepwise.combine Stepise combine
stepwise.forward Stepise forward non-exhaustive greedy search, calculates the optimum value of the discount factor.
subject Estimate subject's full network: runs exhaustive search on very node.
symmetric Turns asymetric network into an symmetric network. Helper function to determine the detection of a connection while ignoring directionality.
ttest.nettest Comparing connectivity strenght of two groups with FDR correction.
utestdata Results from v.1.0 for unit tests.