Simulate network structure and data (2024)

simNet {modnets}

R Documentation

Description

Used for generating moderated and unmoderated adjacency matrices, along withdata based on those model structures.

Usage

simNet( N = 100, p = 5, m = FALSE, m2 = 0.1, b1 = NULL, b2 = NULL, sparsity = 0.5, intercepts = NULL, nIter = 250, msym = FALSE, onlyDat = FALSE, pbar = TRUE, div = 10, gibbs = TRUE, ordinal = FALSE, nLevels = 5, mord = FALSE, time = TRUE, mbinary = FALSE, minOrd = 3, m1 = NULL, m1_range = NULL, m2_range = c(0.1, 0.3), modType = "none", lags = NULL, V = 2, skewErr = FALSE, onlyNets = FALSE, netArgs = NULL, nCores = 1, cluster = "SOCK", getChains = FALSE, const = 1.5, fixedPar = NULL, V2 = 1, ...)

Arguments

`N`	Numeric value. Total number of subjects.
`p`	Numeric value. Total number of nodes (excluding moderator).
`m`	If a value is provided, a moderator is generated and named `M`in the resultant data. If `TRUE`, then a normal distribution with amean of 0 will be used to generate the initial value of `m`, whichwill serve as the population mean for `m` throughout the simulation.If a numeric value is provided, then this will serve as the populationmean, and all subsequent draws will be taken from a normal distributionwith that mean. If `m = "binary"`, then this will simply set theargument `mbinary = TRUE`. If `m = "ordinal"`, this will set`mord = TRUE`. To simulate `m` from a skewed distribution, thereare two options: if `m = "skewed"`, then the `alpha` parameter ofthe `sn::rmsn` will automatically be set to 3.Alternatively, a vector of length two can be supplied, containing theelement `"skewed"` as well as the desired value of `alpha`.Lastly, a function can be provided for `m` if the user wishes tosample `m` from another distribution. The requirement is that thefunction have only one argument, and only returns a single numeric value.The input of the argument should be the location parameter of the desiredsampling distribution.
`m2`	Numeric. If `m2 >= 1`, then this will determine the number ofinteraction effects between the moderator and some node in the network. Ifa value between 0 and 1 is provided, then this determines the probabilityof any given edge being moderated by the moderator.
`b1`	Can provide an adjacency matrix to use for generating data.
`b2`	Can provide an interaction matrix for generated moderated data.
`sparsity`	Numeric value between 0 and 1. Determines the sparsity ofsampled network matrices.
`intercepts`	A vector of means for sampling node values.
`nIter`	Number of iterations for generating each instance of a datapointwith the Gibbs sampler.
`msym`	If `TRUE` then will force the interaction matrix to besymmetric.
`onlyDat`	If `TRUE` then the function only returns the simulateddata.
`pbar`	If `TRUE` then a progress bar will be shown as samples aregenerated.
`div`	A value to use as a sign that the sampler diverged. Can beincreased based on expected range of values. If a datapoint is larger than`div`, then the sampler will stop.
`gibbs`	If `TRUE`, then Gibbs sampling will be used. Otherwise,data are generated from the `mvtnorm::rmvnorm`function based on the partial correlation matrix that is created.
`ordinal`	Logical. Determines whether to generate ordinal values or not.
`nLevels`	Number of levels for the ordinal variables. Only relevant if`ordinal` is not `FALSE`.
`mord`	Logical. Determines whether the moderator variable should besimulated as ordinal.
`time`	If `TRUE` then the time it takes to simulate the data isprinted to screen at the end of the sampling.
`mbinary`	Logical. Determines whether the moderator should be a binaryvariable.
`minOrd`	The minimum number of unique values allowed for each variable.
`m1`	Functions similarly to `m2`, except that this argument refersto the number/probability of main effects of the moderator on any givennode.
`m1_range`	Numeric vector of length 2. The range of values for moderatormain effect coefficients.
`m2_range`	Numeric vector of length 2. The range of values for moderatorinteraction effect coefficients.
`modType`	Determines the type of moderation to employ, such as`"none", "full", "partial"`. If `modType = "full"`, then for anyinteraction terms there will be full moderation, such that all pairwiserelationships for moderated paths will be set to zero. If `modType = "partial"`, then pairwise edges for moderated paths will always be nonzero.If `modType = "none"`, no constraints will be applied (e.g., couldproduce a mix between full and partial moderation).
`lags`	If `TRUE` or 1, then arguments are rerouted to the`mlGVARsim` function to simulate temporal data for a singleindividual.
`V`	Numeric, either 1 or 2. Determines whether to randomize the order ofsimulating node values at each iteration of the Gibbs sampler. If `V = 2`, then the order is randomized at each iteration. If `V = 1`, thenthe sampler moves through the nodes from the first to the last in order ateach iteration.
`skewErr`	The skewness parameter for the `alpha` argument in the`sn::rmsn` function. Only relevant when `gibbs = FALSE` and no moderator is specified.
`onlyNets`	If `TRUE` then only the network models are returned,without the data. Could be used to create random models and then simulatedata by another method.
`netArgs`	Only for use by the internal function`modnets:::simNet2`, which serves as a wrapper for the currentfunction to prevent it from failing.
`nCores`	Numeric value indicating the number of CPU cores to use for theresampling. If `TRUE`, then the`parallel::detectCores` function will beused to maximize the number of cores available.
`cluster`	Character vector indicating which type of parallelization touse, if `nCores > 1`. Options include `"mclapply"` and`"SOCK"`.
`getChains`	Logical. Determines whether to return the data-generatingchains from the Gibbs sampler.
`const`	Numeric. The constant to be used by the internal`modnets:::simPcor` function.
`fixedPar`	Numeric. If provided, then this will be set as thecoefficient value for all edges in the network. Provides a way tostandardize the parameter values while varying the sparsity of the network.If `length(fixedPar) == 1`, then the same value will be used for allparameters. If `length(fixedPar) == 2`, then the first value will befor pairwise relationships, and the second value will be for interactionterms.
`V2`	If `V2 = 1` and `m2` is between 0 and 1, the number ofinteraction terms in the model will be determined by multiplying `m2`with the number of elements in the interaction matrix and taking the`ceiling`.
`...`	Additional arguments.

Details

If no moderator is specified then data can be generated directly from apartial correlation matrix by setting gibbs = FALSE, which producesfast simulation results. Alternatively, a Gibbs sampler is used to generatedata, which is the default option. For moderated networks, Gibbs sampling isthe only method available.

Value

Simulated network models as well as data generated from those models.For GGMs, model matrices are always symmetric. For temporal networks (whenlags = 1), columns predict rows.

Warning

Importantly, the Gibbs sampler can easily diverge given certain modelparameters. Generating network data based on moderator variables canproduce data that quickly take on large values due to the presence ofmultiplicative terms. If the simulation fails, first simply try re-runningthe function with a different seed; this will often be sufficient to solvethe problem when default parameters are specified. Additionally, one canincrease the value of div, in case the sampler only divergesslightly or simply produced an anomalous value. This raises the thresholdof tolerated values before the sampler stops. If supplying user-generatedmodel matrices (for the b1 and/or b2 arguments) and thefunction continues to fail, you will likely need to change the parametervalues in those matrices, as it may not be possible to simulate data underthe given values. If simulating the model matrices inside the function (asis the default) and the function continues to fail, try adjusting thefollowing parameters:

Try reducing the value of m2to specify fewer interactions.
Try reducing a range with a smallermaximum for m2_range, to adjust the range of interactioncoefficients.
Try adjusting the corresponding main effect parametersfor the moderator, m1 and m1_range.
Try settingmodType = "full" to reduce the number of main effect parameters.
Try setting a low value(s) for fixedPar, in order to provideparameter values that are known to be lower

An alternative approach could be to use the internal functionsimNet2, which is a wrapper designed to re-run simNet when itfails and automatically adjust simulation parameters such as div tothoroughly test a given parameterization scheme. This function can beaccessed via modnets:::simNet2. There is not documentation for thisfunction, so it is recommended to look at the source code if one wishes touse it This wrapper is also used inside the mnetPowerSim function.

Examples

# Generate a moderated GGM along with dataset.seed(1)x <- simNet(N = 100, p = 3, m = TRUE)net(x) # Get data-generating adjacency matrixnetInts(x) # Get data-generating interaction matrixplot(x) # Plot the moderated network that generated the data# Generate a single-subject GVAR model with dataset.seed(1)x <- simNet(N = 500, p = 3, m = TRUE, lags = 1)net(x, n = 'temporal') # Get the data-generating time-lagged adjacency matrixnet(x, n = 'contemporaneous') # Get the data-generating standardized residual covariance matrixplot(x, which.net = 'beta') # 'beta' is another way of referring to the temporal networkplot(x, which.net = 'pcc') # 'pcc' is another way of referring to the contemporaneous network

[Package modnets version 0.9.0 Index]