Multivariate normal design specification

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.

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

Details

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

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.

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.