genCorGen {simstudy}

Create multivariate (correlated) data - for general distributions

Description

Create multivariate (correlated) data - for general distributions

Usage

genCorGen(
  n,
  nvars,
  params1,
  params2 = NULL,
  dist,
  rho,
  corstr,
  corMatrix = NULL,
  wide = FALSE,
  cnames = NULL,
  method = "copula",
  idname = "id"
)

Arguments

n

Number of observations

nvars

Number of variables

params1

A single vector specifying the mean of the distribution. The vector is of length 1 if the mean is the same across all observations, otherwise the vector is of length nvars. In the case of the uniform distribution the vector specifies the minimum.

params2

A single vector specifying a possible second parameter for the distribution. For the normal distribution, this will be the variance; for the gamma distribution, this will be the dispersion; and for the uniform distribution, this will be the maximum. The vector is of length 1 if the mean is the same across all observations, otherwise the vector is of length nvars.

dist

A string indicating "binary", "poisson" or "gamma", "normal", or "uniform".

rho

Correlation coefficient, -1 <= rho <= 1. Use if corMatrix is not provided.

corstr

Correlation structure of the variance-covariance matrix defined by sigma and rho. Options include "cs" for a compound symmetry structure and "ar1" for an autoregressive structure.

corMatrix

Correlation matrix can be entered directly. It must be symmetrical and positive semi-definite. It is not a required field; if a matrix is not provided, then a structure and correlation coefficient rho must be specified.

wide

The layout of the returned file - if wide = TRUE, all new correlated variables will be returned in a single record, if wide = FALSE, each new variable will be its own record (i.e. the data will be in long form). Defaults to FALSE.

cnames

Explicit column names. A single string with names separated by commas. If no string is provided, the default names will be V#, where # represents the column.

method

Two methods are available to generate correlated data. (1) "copula" uses the multivariate Gaussian copula method that is applied to all other distributions; this applies to all available distributions. (2) "ep" uses an algorithm developed by Emrich and Piedmonte (1991).

idname

Character value that specifies the name of the id variable.

Value

data.table with added column(s) of correlated data

References

Emrich LJ, Piedmonte MR. A Method for Generating High-Dimensional Multivariate Binary Variates. The American Statistician 1991;45:302-4.

Examples

set.seed(23432)
lambda <- c(8, 10, 12)

genCorGen(100, nvars = 3, params1 = lambda, dist = "poisson", rho = .7, corstr = "cs")
genCorGen(100, nvars = 3, params1 = 5, dist = "poisson", rho = .7, corstr = "cs")
genCorGen(100, nvars = 3, params1 = lambda, dist = "poisson", rho = .7, corstr = "cs", wide = TRUE)
genCorGen(100, nvars = 3, params1 = 5, dist = "poisson", rho = .7, corstr = "cs", wide = TRUE)

genCorGen(100,
  nvars = 3, params1 = lambda, dist = "poisson", rho = .7, corstr = "cs",
  cnames = "new_var"
)
genCorGen(100,
  nvars = 3, params1 = lambda, dist = "poisson", rho = .7, corstr = "cs",
  wide = TRUE, cnames = "a, b, c"
)