R: Multivariate normal design specification

simdesign_mvtnorm {simdata}

R Documentation

Multivariate normal design specification

Description

Stores information necessary to simulate and visualize datasets based on underlying distribution multivariate normal distribution Z.

Usage

simdesign_mvtnorm(
  relations_initial,
  mean_initial = 0,
  sd_initial = 1,
  is_correlation = TRUE,
  method = "svd",
  name = "Multivariate-normal based simulation design",
  ...
)

Arguments

`relations_initial`	Correlation / Covariance matrix of the initial multivariate Normal distribution `Z`.
`mean_initial`	Vector of mean values of the initial multivariate Normal distribution `Z`. Dimension needs to correspond to dimension of `relations`.
`sd_initial`	Vector of standard deviations of the initial multivariate Normal distribution Z. Dimension needs to correspond to dimension of `relations`. Overriden by suqare root of diagonal elements of `relations` if `is_correlation` is FALSE.
`is_correlation`	If TRUE, then `relations` specifies a correlation matrix (default, this type of specification is usually more natural than specifying a covariance matrix). Otherwise, `relations` specifies a covariance matrix whose square root diagonal elements override `sd_initial`.
`method`	`method` argument of `mvtnorm::rmvnorm`.
`name`	Character, optional name of the simulation design.
`...`	Further arguments are passed to the `simdesign` constructor.

Details

This S3 class implements a simulation design based on an underlying multivariate normal distribution by creating a generator function based on mvtnorm::rmvnorm.

Value

List object with class attribute "simdesign_mvtnorm" (S3 class), inheriting from "simdesign". It contains the same entries as a simdesign object but in addition the following entries:

mean_initial
sd_initial
cor_initial: Initial correlation matrix of multivariate normal distribution

Data Generation

Data will be generated by simulate_data using the following procedure:

The underlying data matrix Z is sampled from a multivariate Normal distribution (number of dimensions specified by dimensions of relations).
Z is then transformed into the final dataset X by applying the transform_initial function to Z.
X is post-processed if specified.

Note

Note that relations specifies the correlation / covariance of the underlying Normal data Z and thus does not directly translate into correlations between the variables of the final datamatrix X.