Package 'DGM' reference manual

Title:	Dynamic Graphical Models
Description:	Dynamic graphical models for multivariate time series data to estimate directed dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et al. (2017) <doi:10.1016/j.neuroimage.2018.03.074>.
Authors:	Simon Schwab <[email protected]>, Ruth Harbord <[email protected]>, Lilia Costa <[email protected]>, Thomas Nichols <[email protected]>
Maintainer:	Simon Schwab <[email protected]>
License:	GPL-3
Version:	1.7.4
Built:	2024-11-07 04:27:02 UTC
Source:	https://github.com/schw4b/dgm

Performes a binomial test with FDR correction for network edge occurrence.

Description

Performes a binomial test with FDR correction for network edge occurrence.

Usage

binom.nettest(adj, alter = "two.sided", fdr = 0.05)
binom.nettest(adj, alter = "two.sided", fdr = 0.05)

Arguments

`adj`	adjacency matrix, nodes x nodes x subj, or nodes x nodes x runs x subj.
`alter`	type of binomial test, "two.sided" (default), "less", or "greater"
`fdr`	false discovery rate (FDR) control, default is 0.05.

Value

store list with results.

Examples

# Generate some sample binary 5-node network structures for N=20, then perform
# significance testing.
N=20
x = rmdiag(array(rbinom(n=5*5*N, size=1, prob=0.10), dim=c(5,5,N)))
x[1,2,2:N]=1; x[2,3,seq(1,N,2)]=1 # add some consitent edges
A = apply(x, c(1,2), mean)
l = binom.nettest(x)

# Generate some sample binary 5-node network structures for N=20, then perform
# significance testing.
N=20
x = rmdiag(array(rbinom(n=5*5*N, size=1, prob=0.10), dim=c(5,5,N)))
x[1,2,2:N]=1; x[2,3,seq(1,N,2)]=1 # add some consitent edges
A = apply(x, c(1,2), mean)
l = binom.nettest(x)

Mean centers timeseries in a 2D array timeseries x nodes, i.e. each timeseries of each node has mean of zero.

Description

Mean centers timeseries in a 2D array timeseries x nodes, i.e. each timeseries of each node has mean of zero.

Usage

center(X)
center(X)

Arguments

`X`	2D array with dimensions timeseries x nodes.

Value

M 2D array.

Examples

data("utestdata")
myts=center(myts)


data("utestdata")
myts=center(myts)

Threshold correlation matrix to match a given number of edges.

Description

Threshold correlation matrix to match a given number of edges.

Usage

cor2adj(R, n)
cor2adj(R, n)

Arguments

`R`	correlation matrix.
`n`	number of edges.

Value

A thresholded matrix.

Mean correlation of time series across subjects.

Description

Mean correlation of time series across subjects.

Usage

corTs(ts)
corTs(ts)

Arguments

`ts`	a 3D time series time series x nodes x subjects.

Value

M correlation matrix.

Examples

# create some sample data with 200 samples,
# 5 nodes, and 2 subjects
ts = array(rnorm(200*5*2), dim=c(200,5,2))
M = corTs(ts)

# create some sample data with 200 samples,
# 5 nodes, and 2 subjects
ts = array(rnorm(200*5*2), dim=c(200,5,2))
M = corTs(ts)

A group is a list containing restructured data from subejcts for easier group analysis.

Description

A group is a list containing restructured data from subejcts for easier group analysis.

Usage

dgm.group(subj)
dgm.group(subj)

Arguments

subj

a list of subjects.

Value

group a list.

Examples

# create some sample data with 200 samples,
# 3 nodes, and 2 subjects
ts = array(rnorm(200*3*2), dim=c(200,3,2))
mysubs=list()
mysubs[[1]]=subject(ts[,,1])
mysubs[[2]]=subject(ts[,,2])
g=dgm.group(mysubs)

# create some sample data with 200 samples,
# 3 nodes, and 2 subjects
ts = array(rnorm(200*3*2), dim=c(200,3,2))
mysubs=list()
mysubs[[1]]=subject(ts[,,1])
mysubs[[2]]=subject(ts[,,2])
g=dgm.group(mysubs)

Quick diagnostics on delta.

Description

Quick diagnostics on delta.

Usage

diag.delta(path, id, nodes)
diag.delta(path, id, nodes)

Arguments

`path`	path to results files.
`id`	subject identifier.
`nodes`	number of nodes.

Value

x array node model's delta

Calculate the log predictive likelihood for a specified set of parents and a fixed delta.

Description

Calculate the log predictive likelihood for a specified set of parents and a fixed delta.

Usage

dlm.lpl(Yt, Ft, delta, priors = priors.spec())
dlm.lpl(Yt, Ft, delta, priors = priors.spec())

Arguments

`Yt`	the vector of observed time series, length `T`.
`Ft`	the matrix of covariates, dim = number of thetas (`p`) x number of time points (`T`), usually a row of 1s to represent an intercept and the time series of the parent nodes.
`delta`	discount factor (scalar).
`priors`	list with prior hyperparameters.

Value

`mt`	the vector or matrix of the posterior mean (location parameter), dim = `p x T`.
`Ct`	and `CSt` the posterior scale matrix `C_{t}` is `C_{t} = C*_{t} x S_{t}`, with dim = `p x p x T`, where `S_{t}` is a point estimate for the observation variance `phi^{-1}`
`Rt`	and `RSt` the prior scale matrix `R_{t}` is `R_{t} = R*_{t} x S_{t-1}`, with dim = `p x p x T`, where `S_{t-1}` is a point estimate for the observation variance `phi^{-1}` at the previous time point.
`nt`	and `dt` the vectors of the updated hyperparameters for the precision `phi` with length `T`.
`S`	the vector of the point estimate for the observation variance `phi^{-1}` with length `T`.
`ft`	the vector of the one-step forecast location parameter with length `T`.
`Qt`	the vector of the one-step forecast scale parameter with length `T`.
`ets`	the vector of the standardised forecast residuals with length `T`, $\newline$ defined as `(Y_{t} - f_{t}) / sqrt (Q_{t})`.
`lpl`	the vector of the Log Predictive Likelihood with length `T`.

References

West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York.

Examples

data("utestdata")
Yt = myts[,1]
Ft = t(cbind(1,myts[,2:5]))
m = dlm.lpl(Yt, Ft, 0.7)


data("utestdata")
Yt = myts[,1]
Ft = t(cbind(1,myts[,2:5]))
m = dlm.lpl(Yt, Ft, 0.7)

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.

Description

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.

Usage

dlm.retro(mt, CSt, RSt, nt, dt)
dlm.retro(mt, CSt, RSt, nt, dt)

Arguments

`mt`	the vector or matrix of the posterior mean (location parameter), dim = `p x T`, where `p` is the number of thetas (at any time `t`) and `T` is the number of time points
`CSt`	the posterior scale matrix with dim = `p x p x T` (unscaled by the observation variance)
`RSt`	the prior scale matrix with dim = `p x p x T` (unscaled by the observation variance)
`nt`	vector of the updated hyperparameters for the precision `phi` with length `T`
`dt`	vector of the updated hyperparameters for the precision `phi` with length `T`

Value

smt = the location parameter of the retrospective distribution with dimension p x T sCt = the scale matrix of the retrospective distribution with dimension p x p x T

C++ implementation of the dlm.lpl

Description

C++ implementation of the dlm.lpl

Usage

dlmLplCpp(Yt_, Ft_, delta, m0_, CS0_, n0, d0)
dlmLplCpp(Yt_, Ft_, delta, m0_, CS0_, n0, d0)

Arguments

`Yt_`	the vector of observed time series
`Ft_`	the matrix of covariates
`delta`	discount factor
`m0_`	the value of the prior mean
`CS0_`	controls the scaling of the prior variance
`n0`	prior hypermarameter
`d0`	prior hypermarameter

A function for an exhaustive search, calculates the optimum value of the discount factor.

Description

A function for an exhaustive search, calculates the optimum value of the discount factor.

Usage

exhaustive.search(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  cpp = TRUE,
  priors = priors.spec()
)
exhaustive.search(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  cpp = TRUE,
  priors = priors.spec()
)

Arguments

`Data`	Dataset with dimension number of time points T x Number of nodes Nn.
`node`	The node to find parents for.
`nbf`	Log Predictive Likelihood will sum from (and including) this time point.
`delta`	a vector of potential values for the discount factor.
`cpp`	boolean true (default): fast C++ implementation, false: native R code.
`priors`	list with prior hyperparameters.

Value

model.store a matrix with the model, LPL and chosen discount factor for all possible models. runtime an estimate of the run time of the function, using proc.time().

Examples

data("utestdata")
result=exhaustive.search(myts,3)

data("utestdata")
result=exhaustive.search(myts,3)

Get adjacency and associated likelihoods (LPL) and disount factros (df) of winning models.

Description

Get adjacency and associated likelihoods (LPL) and disount factros (df) of winning models.

Usage

getAdjacency(winner, nodes)
getAdjacency(winner, nodes)

Arguments

`winner`	2D matrix.
`nodes`	number of nodes.

Value

adj, 2D adjacency matrix.

Checks results and returns job number for incomplete nodes.

Description

Checks results and returns job number for incomplete nodes.

Usage

getIncompleteNodes(path, ids, Nr, Nn)
getIncompleteNodes(path, ids, Nr, Nn)

Arguments

`path`	path to results.
`ids`	subjects ids.
`Nr`	Number of runs.
`Nn`	Number of nodes.

Value

jobs job numbers

Extract specific parent model with assocated df and ME from complete model space.

Description

Extract specific parent model with assocated df and ME from complete model space.

Usage

getModel(models, parents)
getModel(models, parents)

Arguments

`models`	a 2D model matrix.
`parents`	a vector with parent nodes.

Value

mod specific parent model.

Examples

data("utestdata")
r=exhaustive.search(myts,3)
# get model with parents 1, 2, and 4.
m=getModel(r$model.store,c(1,2,4))

data("utestdata")
r=exhaustive.search(myts,3)
# get model with parents 1, 2, and 4.
m=getModel(r$model.store,c(1,2,4))

Get model number from a set of parents.

Description

Get model number from a set of parents.

Usage

getModelNr(models, parents)
getModelNr(models, parents)

Arguments

`models`	a 2D model matrix.
`parents`	a vector with parent nodes.

Value

nr model number.

Get winner network by maximazing log predictive likelihood (LPL) from a set of models.

Description

Get winner network by maximazing log predictive likelihood (LPL) from a set of models.

Usage

getWinner(models, nodes)
getWinner(models, nodes)

Arguments

`models`	2D matrix, or 3D models x node.
`nodes`	number of nodes.

Value

winner array with highest scored model(s).

Plots network as adjacency matrix.

Description

Plots network as adjacency matrix.

Usage

gplotMat(
  adj,
  title = NULL,
  colMapLabel = NULL,
  hasColMap = NULL,
  lim = c(0, 1),
  gradient = c("white", "orange", "red"),
  nodeLabels = waiver(),
  axisTextSize = 12,
  xAngle = 0,
  titleTextSize = 12,
  barWidth = 1,
  textSize = 12
)
gplotMat(
  adj,
  title = NULL,
  colMapLabel = NULL,
  hasColMap = NULL,
  lim = c(0, 1),
  gradient = c("white", "orange", "red"),
  nodeLabels = waiver(),
  axisTextSize = 12,
  xAngle = 0,
  titleTextSize = 12,
  barWidth = 1,
  textSize = 12
)

Arguments

`adj`	2D adjacency matrix.
`title`	title.
`colMapLabel`	label for colormap.
`hasColMap`	FALSE turns off color map, default is NULL (on).
`lim`	vector with min and max value, data outside this range will be removed.
`gradient`	gradient colors.
`nodeLabels`	node labels.
`axisTextSize`	text size of the y and x tick labels.
`xAngle`	orientation of the x tick labels.
`titleTextSize`	text size of the title.
`barWidth`	width of the colorbar.
`textSize`	width of the colorbar.

Examples

# Generate some sample binary 5-node network structures for N=20, then compute
# proportion at each edge
N=20
x = array(rbinom(n=5*5*N, size=1, prob=0.30), dim=c(5,5,N))
A = apply(x, c(1,2), mean)

gplotMat(A, title = "network", colMapLabel = '%', barWidth = 0.3)


# Generate some sample binary 5-node network structures for N=20, then compute
# proportion at each edge
N=20
x = array(rbinom(n=5*5*N, size=1, prob=0.30), dim=c(5,5,N))
A = apply(x, c(1,2), mean)

gplotMat(A, title = "network", colMapLabel = '%', barWidth = 0.3)

Merges forward and backward model store.

Description

Merges forward and backward model store.

Usage

mergeModels(fw, bw)
mergeModels(fw, bw)

Arguments

`fw`	forward model.
`bw`	backward model.

Value

m model store.

A function to generate all the possible models.

Description

A function to generate all the possible models.

Usage

model.generator(Nn, node)
model.generator(Nn, node)

Arguments

`Nn`	number of nodes; the number of columns of the dataset can be used.
`node`	The node to find parents for.

Value

output.model = a matrix with dimensions (Nn-1) x number of models, where number of models = 2^(Nn-1).

Examples

m=model.generator(5,1)

m=model.generator(5,1)

Network simulation data.

Description

Simulation 22 5 node net from Smith et al. 2011 (only first subject).

Runs exhaustive search on a single node and saves results in txt file.

Description

Runs exhaustive search on a single node and saves results in txt file.

Usage

node(
  X,
  n,
  id = NULL,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  cpp = TRUE,
  priors = priors.spec(),
  path = getwd(),
  method = "exhaustive"
)
node(
  X,
  n,
  id = NULL,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  cpp = TRUE,
  priors = priors.spec(),
  path = getwd(),
  method = "exhaustive"
)

Arguments

`X`	array with dimensions timeseries x nodes.
`n`	node number.
`id`	subject ID. If set, results are saved to a txt file.
`nbf`	Log Predictive Likelihood will sum from (and including) this time point.
`delta`	a vector of potential values for the discount factor.#'
`cpp`	boolean true (default): fast C++ implementation, false: native R code.
`priors`	list with prior hyperparameters.
`path`	a path where results are written.
`method`	can be exhaustive (default), forward, backward, or both.

Value

store list with results.

Patel.

Description

Patel.

Usage

patel(X, lower = 0.1, upper = 0.9, bin = 0.75, TK = 0, TT = 0)
patel(X, lower = 0.1, upper = 0.9, bin = 0.75, TK = 0, TT = 0)

Arguments

`X`	time x node 2D matrix.
`lower`	percentile cuttoff.
`upper`	percentile cuttoff for 0-1 scaling.
`bin`	threshold for conversion to binary values.
`TK`	significance threshold for connection strength kappa.
`TT`	significance threshold for direction tau.

Value

PT list with strengths kappa, direction tau, and net structure.

Examples

# Generate some sample data
x=array(rnorm(200*5), dim=c(200,5))
p=patel(x)
# Generate some sample data
x=array(rnorm(200*5), dim=c(200,5))
p=patel(x)

A group is a list containing restructured data from subejcts for easier group analysis.

Description

A group is a list containing restructured data from subejcts for easier group analysis.

Usage

patel.group(subj)
patel.group(subj)

Arguments

subj

a list of subjects.

Value

group a list.

Examples

# create some sample data with 200 samples,
# 3 nodes, and 2 subjects
ts = array(rnorm(200*3*2), dim=c(200,3,2))
mysubs=list()
mysubs[[1]]=patel(ts[,,1])
mysubs[[2]]=patel(ts[,,2])
g=patel.group(mysubs)
# create some sample data with 200 samples,
# 3 nodes, and 2 subjects
ts = array(rnorm(200*3*2), dim=c(200,3,2))
mysubs=list()
mysubs[[1]]=patel(ts[,,1])
mysubs[[2]]=patel(ts[,,2])
g=patel.group(mysubs)

Performance of estimates, such as sensitivity, specificity, and more.

Description

Performance of estimates, such as sensitivity, specificity, and more.

Usage

perf(x, true)
perf(x, true)

Arguments

`x`	estimated binary network matrix.
`true`	true binary network matrix.

Value

p list with results.

Examples

trueNet=matrix(c(0,0,0,1,0,0,0,1,0),3,3)
am=matrix(c(0,0,0,1,0,1,0,1,0),3,3)
p=perf(am, trueNet)
trueNet=matrix(c(0,0,0,1,0,0,0,1,0),3,3)
am=matrix(c(0,0,0,1,0,1,0,1,0),3,3)
p=perf(am, trueNet)

Specify the priors. Without inputs, defaults will be used.

Description

Specify the priors. Without inputs, defaults will be used.

Usage

priors.spec(m0 = 0, CS0 = 3, n0 = 0.001, d0 = 0.001)
priors.spec(m0 = 0, CS0 = 3, n0 = 0.001, d0 = 0.001)

Arguments

`m0`	the value of the prior mean at time `t=0`, scalar (assumed to be the same for all nodes). The default is zero.
`CS0`	controls the scaling of the prior variance matrix `C_{0}` at time `t=0`. The default is 3, giving a non-informative prior for `C_{0}, 3 x (p x p)` identity matrix. `p` is the number of thetas.
`n0`	prior hyperparameter of precision `phi ~ G(n_{0}/2; d_{0}/2)`. The default is a non-informative prior, with `n0 = d0 = 0.001`. `n0` has to be higher than 0.
`d0`	prior hyperparameter of precision `phi ~ G(n_{0}/2; d_{0}/2)`. The default is a non-informative prior, with `n0 = d0 = 0.001`.

Details

At time t=0, (theta_{0} | D_{0}, phi) ~ N(m_{0},C*_{0} x phi^{-1}), where D_{0} denotes the set of initial information.

Value

priors a list with the prior hyperparameters. Relevant to dlm.lpl, exhaustive.search, node, subject.

References

West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York.

Examples

pr=priors.spec()
pr=priors.spec(n0=0.002)
pr=priors.spec()
pr=priors.spec(n0=0.002)

Comparing two population proportions on the network with FDR correction.

Description

Comparing two population proportions on the network with FDR correction.

Usage

prop.nettest(x1, n1, x2, n2, alpha = 0.05, fdr = 0.05)
prop.nettest(x1, n1, x2, n2, alpha = 0.05, fdr = 0.05)

Arguments

`x1`	network matrix with successes in group 1.
`n1`	sample size group 1.
`x2`	network matrix with successes in group 2.
`n2`	sample size group 2.
`alpha`	alpha level for uncorrected test.
`fdr`	alpha level for FDR.

Value

store List with test statistics and p-values.

Get pruned adjacency network.

Description

Get pruned adjacency network.

Usage

pruning(adj, models, winner, e = 20)
pruning(adj, models, winner, e = 20)

Arguments

`adj`	list with network adjacency from getAdjacency().
`models`	list of models.
`winner`	matrix 2D with winning models.
`e`	bayes factor for network pruning.

Value

thr list with pruned network adjacency.

Examples

data("utestdata")
# select only 3-nodes to speed-up this example
sub=subject(myts[,1:3])
p=pruning(sub$adj, sub$models, sub$winner)
data("utestdata")
# select only 3-nodes to speed-up this example
sub=subject(myts[,1:3])
p=pruning(sub$adj, sub$models, sub$winner)

Randomization test for Patel's kappa. Creates a distribution of values kappa under the null hypothesis.

Description

Randomization test for Patel's kappa. Creates a distribution of values kappa under the null hypothesis.

Usage

rand.test(X, alpha = 0.05, K = 1000)
rand.test(X, alpha = 0.05, K = 1000)

Arguments

`X`	time x node x subjects 3D matrix.
`alpha`	sign. level
`K`	number of randomizations, default is 1000.

Value

stat lower and upper significance thresholds.

Examples

# create some sample data with 200 samples,
# 3 nodes, and 2 subjects
ts = array(rnorm(200*3*5), dim=c(200,3,5))
mysubs=list()
mysubs[[1]]=patel(ts[,,1])
mysubs[[2]]=patel(ts[,,2])
mysubs[[3]]=patel(ts[,,3])
mysubs[[4]]=patel(ts[,,4])
mysubs[[5]]=patel(ts[,,5])
g=patel.group(mysubs)
r=rand.test(rmdiag(g$kappa), K=100)
# create some sample data with 200 samples,
# 3 nodes, and 2 subjects
ts = array(rnorm(200*3*5), dim=c(200,3,5))
mysubs=list()
mysubs[[1]]=patel(ts[,,1])
mysubs[[2]]=patel(ts[,,2])
mysubs[[3]]=patel(ts[,,3])
mysubs[[4]]=patel(ts[,,4])
mysubs[[5]]=patel(ts[,,5])
g=patel.group(mysubs)
r=rand.test(rmdiag(g$kappa), K=100)

Reads single subject's network from txt files.

Description

Reads single subject's network from txt files.

Usage

read.subject(path, id, nodes, modelStore = TRUE)
read.subject(path, id, nodes, modelStore = TRUE)

Arguments

`path`	path.
`id`	identifier to select all subjects' nodes, e.g. pattern containing subject ID and session number.
`nodes`	number of nodes.
`modelStore`	can be set to false to save memory.

Value

store list with results.

Reshapes a 2D concatenated time series into 3D according to no. of subjects and volumes.

Description

Reshapes a 2D concatenated time series into 3D according to no. of subjects and volumes.

Usage

reshapeTs(ts, N, V)
reshapeTs(ts, N, V)

Arguments

`ts`	a 2D time series volumes x nodes.
`N`	No. of subjects.
`V`	No. of volumes.

Value

M 3D matrix, time series x nodes x subjects.

Examples

# Let's say subjects are concatenated in a 2D matrix
# (samples x nodes), with each having 200 samples.
# generate some sample data
N=20
Nn=5
x = array(rnorm(200*N*Nn), dim=c(200*N,Nn))
ts = reshapeTs(x,N,200)

# Let's say subjects are concatenated in a 2D matrix
# (samples x nodes), with each having 200 samples.
# generate some sample data
N=20
Nn=5
x = array(rnorm(200*N*Nn), dim=c(200*N,Nn))
ts = reshapeTs(x,N,200)

Removes diagonal of NA's from matrix.

Description

Removes diagonal of NA's from matrix.

Usage

rmdiag(M)
rmdiag(M)

Arguments

M

Matrix

Value

matrix with diagonal of 0's.

Examples

M=array(rnorm(3*3), dim=c(3,3))
M[as.logical(diag(3))] = NA
M=rmna(M)
M=array(rnorm(3*3), dim=c(3,3))
M[as.logical(diag(3))] = NA
M=rmna(M)

Removes NAs from matrix.

Description

Removes NAs from matrix.

Usage

rmna(M)
rmna(M)

Arguments

M

Matrix

Value

matrix with NAs removed.

Examples

M=array(NA, dim=c(3,3))
M[1,2]=0.9
M=rmna(M)
M=array(NA, dim=c(3,3))
M[1,2]=0.9
M=rmna(M)

Removes reciprocal connections in the lower diagnoal of the network matrix.

Description

Removes reciprocal connections in the lower diagnoal of the network matrix.

Usage

rmRecipLow(M)
rmRecipLow(M)

Arguments

`M`	adjacency matrix

Value

M adjacency matrix without reciprocal connections.

Scaling data. Zero centers and scales the nodes (SD=1).

Description

Scaling data. Zero centers and scales the nodes (SD=1).

Usage

scaleTs(X)
scaleTs(X)

Arguments

`X`	time x node 2D matrix, or 3D with subjects as the 3rd dimension.

Value

S centered and scaled matrix.

Examples

# create some sample data
ts = array(rnorm(200*5, mean=5, sd=10), dim=c(200,5))
ts = scaleTs(ts)

# create some sample data
ts = array(rnorm(200*5, mean=5, sd=10), dim=c(200,5))
ts = scaleTs(ts)

Stepise backward non-exhaustive greedy search, calculates the optimum value of the discount factor.

Description

Stepise backward non-exhaustive greedy search, calculates the optimum value of the discount factor.

Usage

stepwise.backward(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  max.break = TRUE,
  priors = priors.spec()
)
stepwise.backward(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  max.break = TRUE,
  priors = priors.spec()
)

Arguments

`Data`	Dataset with dimension number of time points `T` x number of nodes `Nn`.
`node`	The node to find parents for.
`nbf`	The Log Predictive Likelihood will sum from (and including) this time point.
`delta`	A vector of values for the discount factor.
`max.break`	If `TRUE`, the code will break if adding / removing parents does not improve the LPL. If `FALSE`, the code will continue to the zero parent / all parent model. Default is `TRUE`.
`priors`	List with prior hyperparameters.

Value

model.store The parents, LPL and chosen discount factor for the subset of models scored using this method.

Stepise combine

Description

Stepise combine

Usage

stepwise.combine(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  max.break = TRUE,
  priors = priors.spec()
)
stepwise.combine(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  max.break = TRUE,
  priors = priors.spec()
)

Arguments

`Data`	Dataset with dimension number of time points `T` x number of nodes `Nn`.
`node`	The node to find parents for.
`nbf`	The Log Predictive Likelihood will sum from (and including) this time point.
`delta`	A vector of values for the discount factor.
`max.break`	If `TRUE`, the code will break if adding / removing parents does not improve the LPL. If `FALSE`, the code will continue to the zero parent / all parent model. Default is `TRUE`.
`priors`	List with prior hyperparameters.

Value

model.store The parents, LPL and chosen discount factor for the subset of models scored using this method.

Stepise forward non-exhaustive greedy search, calculates the optimum value of the discount factor.

Description

Stepise forward non-exhaustive greedy search, calculates the optimum value of the discount factor.

Usage

stepwise.forward(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  max.break = TRUE,
  priors = priors.spec()
)
stepwise.forward(
  Data,
  node,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  max.break = TRUE,
  priors = priors.spec()
)

Arguments

`Data`	Dataset with dimension number of time points `T` x number of nodes `Nn`.
`node`	The node to find parents for.
`nbf`	The Log Predictive Likelihood will sum from (and including) this time point.
`delta`	A vector of values for the discount factor.
`max.break`	If `TRUE`, the code will break if adding / removing parents does not improve the LPL. If `FALSE`, the code will continue to the zero parent / all parent model. Default is `TRUE`.
`priors`	List with prior hyperparameters.

Value

model.store The parents, LPL and chosen discount factor for the subset of models scored using this method.

Estimate subject's full network: runs exhaustive search on very node.

Description

Estimate subject's full network: runs exhaustive search on very node.

Usage

subject(
  X,
  id = NULL,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  cpp = TRUE,
  priors = priors.spec(),
  path = getwd(),
  method = "exhaustive"
)
subject(
  X,
  id = NULL,
  nbf = 15,
  delta = seq(0.5, 1, 0.01),
  cpp = TRUE,
  priors = priors.spec(),
  path = getwd(),
  method = "exhaustive"
)

Arguments

`X`	array with dimensions timeseries x nodes.
`id`	subject ID. If set, results are saved to a txt file.
`nbf`	Log Predictive Likelihood will sum from (and including) this time point.
`delta`	a vector of potential values for the discount factor.
`cpp`	boolean true (default): fast C++ implementation, false: native R code.
`priors`	list with prior hyperparameters.
`path`	a path where results are written.
`method`	ether exhaustive, foward, backward, or both.

Value

store list with results.

Examples

data("utestdata")
# select only 3-nodes to speed-up this example
sub=subject(myts[,1:3]) 
sub=subject(myts[,1:3], method="both")

data("utestdata")
# select only 3-nodes to speed-up this example
sub=subject(myts[,1:3]) 
sub=subject(myts[,1:3], method="both")

Turns asymetric network into an symmetric network. Helper function to determine the detection of a connection while ignoring directionality.

Description

Turns asymetric network into an symmetric network. Helper function to determine the detection of a connection while ignoring directionality.

Usage

symmetric(M)
symmetric(M)

Arguments

`M`	3D matrix nodes x nodes x subjects

Value

3D matrix nodes x nodes x subjects

Examples

M=array(NA, dim=c(3,3,2))
M[,,1]=matrix(c(0,0,0,1,0,0,0,1,0),3,3)
M[,,2]=matrix(c(0,0,0,1,0,0,0,0,0),3,3)
M_=symmetric(M)
M=array(NA, dim=c(3,3,2))
M[,,1]=matrix(c(0,0,0,1,0,0,0,1,0),3,3)
M[,,2]=matrix(c(0,0,0,1,0,0,0,0,0),3,3)
M_=symmetric(M)

Comparing connectivity strenght of two groups with FDR correction.

Description

Comparing connectivity strenght of two groups with FDR correction.

Usage

ttest.nettest(m, g, alpha = 0.05, fdr = 0.05, perm = FALSE, n_perm = 9999)
ttest.nettest(m, g, alpha = 0.05, fdr = 0.05, perm = FALSE, n_perm = 9999)

Arguments

`m`	matrix with Nn x Nn x N.
`g`	group assignment, vector of type factor of size N.
`alpha`	alpha level for uncorrected test.
`fdr`	FDR alpha level.
`perm`	optional permuation test, default is false.
`n_perm`	number of permutations.

Package 'DGM'

Help Index

Performes a binomial test with FDR correction for network edge occurrence.

Description

Usage

Arguments

Value

Examples

Mean centers timeseries in a 2D array timeseries x nodes, i.e. each timeseries of each node has mean of zero.

Description

Usage

Arguments

Value

Examples

Threshold correlation matrix to match a given number of edges.

Description

Usage

Arguments

Value

Mean correlation of time series across subjects.

Description

Usage

Arguments

Value

Examples

A group is a list containing restructured data from subejcts for easier group analysis.

Description

Usage

Arguments

Value

Examples

Quick diagnostics on delta.

Description

Usage

Arguments

Value

Calculate the log predictive likelihood for a specified set of parents and a fixed delta.

Description

Usage

Arguments

Value

References

Examples

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.

Description

Usage

Arguments

Value

C++ implementation of the dlm.lpl

Description

Usage

Arguments

A function for an exhaustive search, calculates the optimum value of the discount factor.

Description

Usage

Arguments

Value

Examples

Get adjacency and associated likelihoods (LPL) and disount factros (df) of winning models.

Description

Usage

Arguments

Value

Checks results and returns job number for incomplete nodes.

Description

Usage

Arguments

Value

Extract specific parent model with assocated df and ME from complete model space.

Description

Usage

Arguments

Value

Examples

Get model number from a set of parents.

Description

Usage

Arguments

Value

Get winner network by maximazing log predictive likelihood (LPL) from a set of models.