R: Power simulator for cross-sectional and idiographic networks

mnetPowerSim {modnets}

R Documentation

Power simulator for cross-sectional and idiographic networks

Description

Samples data based on several parameters, mainly used to see how different sample sizes perform given various parameterizations when simulating from network models, especially moderated networks. See simNet for more details about arguments as well as the warning about simulations that fail.

Usage

mnetPowerSim(
  niter = 10,
  N = 100,
  p = 5,
  m = FALSE,
  m1 = 0,
  m2 = 0.1,
  sparsity = 0.5,
  lags = NULL,
  trueNet = NULL,
  threshold = TRUE,
  rule = "OR",
  avg = TRUE,
  maxiter = 100,
  saveFits = TRUE,
  saveData = FALSE,
  intercepts = NULL,
  mbinary = FALSE,
  select = NULL,
  vargs = list(),
  type = "g",
  gibbs = TRUE,
  ordinal = FALSE,
  mord = FALSE,
  nLevels = 5,
  minOrd = 3,
  div = 1000,
  modType = "none",
  m1_range = NULL,
  m2_range = c(0.1, 0.3),
  time = TRUE,
  skewErr = FALSE,
  nCores = 1,
  cluster = "mclapply",
  fixedPar = NULL,
  V2 = 1,
  ...
)

Arguments

`niter`	Number of iterations/samples to take for each combination of parameters.
`N`	Numeric value, or vector of sample sizes to generate data with.
`p`	Numeric value, or vector of network sizes.
`m`	If a value is provided then a moderated network will be simulated. See `simNet` for details.
`m1`	Functions similarly to `m2`, except that this argument refers to the number/probability of main effects of the moderator on any given node.
`m2`	Numeric. If `m2 >= 1`, then this will determine the number of interaction effects between the moderator and some node in the network. If a value between 0 and 1 is provided, then this determines the probability of any given edge being moderated by the moderator.
`sparsity`	Numeric value between 0 and 1. Determines the sparsity of sampled network matrices.
`lags`	Determines whether the network should be a temporal network or not. If simulating a temporal network, set to `TRUE` or 1.
`trueNet`	The adjacency matrix of the data-generating network model, or a list containing the adjacency matrix as the first element, and the interaction matrix as the second element.
`threshold`	See corresponding argument in `fitNetwork`. Automatically set to `TRUE` if `select` is not `NULL`.
`rule`	Only applies to GGMs (including between-subjects networks) when a threshold is supplied. The `"AND"` rule will only preserve edges when both corresponding coefficients have p-values below the threshold, while the `"OR"` rule will preserve an edge so long as one of the two coefficients have a p-value below the supplied threshold.
`avg`	See corresponding argument of `netInts`
`maxiter`	If a model fails to be fit, this determines the maximum number of iterations to re-try it before giving up. Will also simulate new datasets at each iteration.
`saveFits`	Logical. Determines whether to save the models fit to each dataset at each iteration.
`saveData`	Logical. Determines whether to save the datasets generated at each iteration.
`intercepts`	A vector of means for sampling node values.
`mbinary`	Logical. Determines whether the moderator should be a binary variable.
`select`	Identifies a variable selection function – either `varSelect` or `resample` – to use for introducing variable selection at each iteration. The usefulness of this is to mimic a real-world situation, wherein the researcher may be interested in seeing how well datasets of different sizes afford models that approximate a true model after employing iterated variable selection. If `TRUE` then this defaults to `"varSelect"`. Highly recommended to use the `vargs` argument to supply necessary information about the parameters of the variable selection process, such as `sampMethod`, `criterion`, etc.
`vargs`	A named list of arguments relevant to the variable selection procedure specified by the `select` argument.
`type`	Can supply a variable selection object, such as the output from either `varSelect` or `modSelect`, can be supplied to choose a specific constrained model to fit on all iterations. This is essentially an alternative to `select`, in that `select` performs variable selection at each iteration, whereas this argument defines a constrained model that is applied at every iteration.
`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.
`mord`	Logical. Determines whether the moderator variable should be simulated as ordinal.
`nLevels`	Number of levels for the ordinal variables. Only relevant if `ordinal` is not `FALSE`.
`minOrd`	The minimum number of unique values allowed for each variable.
`div`	A value to use as a sign that the sampler diverged. Can be increased based on expected range of values. If a datapoint is larger than `div`, then the sampler will stop.
`modType`	Determines the type of moderation to employ, such as `"none", "full", "partial"`. See `simNet` for details.
`m1_range`	Numeric vector of length 2. The range of values for moderator main effect coefficients.
`m2_range`	Numeric vector of length 2. The range of values for moderator interaction effect coefficients.
`time`	If `TRUE` then the time it takes to simulate the data is printed to screen at the end of the sampling.
`skewErr`	The skewness parameter for the `alpha` argument in the `sn::rmsn` function. Only relevant when `gibbs = FALSE` and no moderator is specified.
`nCores`	Numeric value indicating the number of CPU cores to use for the resampling. If `TRUE`, then the `parallel::detectCores` function will be used to maximize the number of cores available.
`cluster`	Character vector indicating which type of parallelization to use, if `nCores > 1`. Options include `"mclapply"` and `"SOCK"`.
`fixedPar`	Numeric. If provided, then this will be set as the coefficient value for all edges in the network. Provides a way to standardize the parameter values while varying the sparsity of the network. If `length(fixedPar) == 1`, then the same value will be used for all parameters. If `length(fixedPar) == 2`, then the first value will be for pairwise relationships, and the second value will be for interaction terms.
`V2`	If `V2 = 1` and `m2` is between 0 and 1, the number of interaction 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

Evaluates how closely an estimated network is with the true network with regards to metrics such as sensitivity, specificity, and precision, among others. Doesn't calculate values for power, but can be used to serve a similar function as a traditional power analysis based on simulated datasets.

Value

Power simulation results

Examples


x <- mnetPowerSim(niter = 10, N = c(100, 200))
summary(x)
plot(x)

[Package modnets version 0.9.0 Index]