mlGVARsim {modnets}

Main workhorse for simulating VAR and mlGVAR data

Description

Affords the generation of simulated data containing multiple timepoint measurements for a number of subjects. Can simulate data with a single moderator as well.

Usage

mlGVARsim(
  nTime = 50,
  nPerson = 10,
  nNode = 3,
  m = NULL,
  m2 = 0.25,
  m1 = 0.7,
  m0 = 1,
  lag = 1,
  thetaVar = NULL,
  mu_SD = NULL,
  init_beta_SD = NULL,
  fixedMuSD = 1,
  shrink_fixed = 0.9,
  propPos = 0.5,
  m1SD = 0.1,
  m2SD = 0.1,
  m1_range = NULL,
  m2_range = NULL,
  shrink_deviation = 0.9,
  getM = FALSE,
  contemporaneous = "wishart",
  GGMsparsity = 0.5,
  mcenter = TRUE,
  skew = FALSE,
  skewErr = FALSE,
  ordinal = FALSE,
  nLevels = 5,
  ordWithin = TRUE,
  minOrd = 3,
  thresholds = NULL,
  mseed = NULL,
  onlyNets = FALSE,
  modType = "none"
)

Arguments

nTime

Numeric value. The number of timepoints to simulate for each individual.

nPerson

The number of subjects to create data for. Can set to 1 to just simulate a single graphical VAR network.

nNode

The number of nodes/variables to simulate. Does not include a moderator if one is specified.

m

Logical. If TRUE, then a moderator variable will be simulated. Various options also available for highly specific moderator specification: "fixed", "random", "mixed1", "mixed2", "ar", "binary", "skewed", "random0", "ordinal".

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.

m1

Functions similarly to m2, except that this argument refers to the number/probability of main effects of the moderator on any given node.

m0

Only relevant when m = "ar". Determines the autoregressive coefficient in the estimated models. Defaults to .3

lag

Numeric value, supposed to indicate the number of lags to simulate models parameters for. Recommended to leave at 1.

thetaVar

Numeric vector containing the variance associated with each node (excluding the moderator) in the contemporaneous network. If NULL, then it is assumed that the variance for each term is 1.

mu_SD

Numeric vector of length 2. The first value determines the standard deviation of the means associated with the temporal data, and the second value determines the standard deviations of the means associated with the between-subjects network.

init_beta_SD

Similar to mu_SD except that it applies to the coefficient estimates.

fixedMuSD

Standard deviation of the random values for the means of the fixed effects.

shrink_fixed

Numeric value to determine the factor by which to shrink sampled beta coefficients for fixed effects. Value between 0 and 1, where higher values are recommended.

propPos

The proportion of edges with a positive sign.

m1SD

Standard deviation of the moderator main effect coefficients.

m2SD

Standard deviation of the moderator interaction effect coefficients.

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.

shrink_deviation

Numeric value to determine the factor by which to shrink contemporaneous coefficients. Value between 0 and 1, where higher values are recommended.

getM

If TRUE, only the data for the moderator, the moderator main effects, and interaction effects are returned.

contemporaneous

Options include "wishart", "randomGGM", "fixed". Determines how the contemporaneous network is sampled. The former two options sample different matrices for each subject, whereas "fixed" only samples one matrix and uses it for all subject contemporaneous networks.

GGMsparsity

Numeric value between 0 and 1. Determines the sparsity of sampled network matrices.

mcenter

If TRUE then the moderator variable is mean-centered.

skew

If TRUE then random values will be generated to represent the skewness of the node distributions. Alternatively, a numeric vector of length nNode can be provided to specify the skewness of each variable.

skewErr

The skewness parameter for the alpha argument in the sn::rmsn function.

ordinal

Logical. Determines whether to sample ordinal variables. If a numeric value is provided, then this will automatically be assigned to the nLevels argument.

nLevels

Number of levels for the ordinal variables. Only relevant if ordinal is not FALSE.

ordWithin

If TRUE, then variables will ordinalized within subjects, rather than across subjects.

minOrd

The minimum number of unique values allowed for each variable.

thresholds

List of length k, where each element is a numeric vector of length (nLevels - 1) containing the splitpoints for grouping each variable into ordered categories.

mseed

Numeric value for the seed to be set when

onlyNets

If TRUE then only the network models are returned, without the data. Could be used to create random models and then simulate data by another method.

modType

Determines the type of moderation to employ, such as "none", "full", "partial". See simNet

Details

Made to simulate data based on pre-specified parameters, possibly for power simulations or other analyses. Output can be used to fit models with either mlGVAR or lmerVAR.

Value

Simulated mlGVAR or VAR data.

See Also

mlGVAR, lmerVAR, simNet, plotNet, net, netInts

Examples

set.seed(1)
x <- mlGVARsim(nTime = 50, nPerson = 10, nNode = 3, m = TRUE)