dineR (version 1.0.1)

data_generator: Data Generator

Description

This functions generates two \(n\) by \(p\) size samples of multivariate normal data. In doing this it also determines and provides the relevant covariance matrices.

Usage

data_generator(n, p, Delta = NULL, case = "sparse", seed = NULL)

Arguments

n

The number of observations generated.

p

The number of dimensions for the generated samples.

Delta

Optional parameter - Provides the differential network that will be used to obtain the sample covariance matrices.

case

Optional parameter - Selects under which case the covariance matrices are determined. Possible cases are: "sparse" - Sparse Case or "asymsparse"- Asymptotically Sparse Case. Defaults to "sparse".

seed

Optional parameter - Allows a seed to be set for reproducibility.

Value

A list of various outputs, namely:

• case - The case used.

• seed_option - The seed provided.

• X - The first multivariate normal sample.

• Y - The second multivariate normal sample.

• Sigma_X - The covariance matrix of X.

• Sigma_Y - The covariance matrix of Y.

• Omega_X - The precision matrix of X.

• Omega_Y - The precision matrix of Y.

• diff_Omega - The difference of precision matrices.

• Delta - The target differential network.

Examples

# NOT RUN {
data <- data_generator(n = 100, p = 50, seed = 123)
data <- data_generator(n = 10, p = 50, case = "asymsparse")
# }