Description Usage Arguments Value Examples
View source: R/data_generator.R
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.
1 | data_generator(n, p, Delta = NULL, case = "sparse", seed = NULL)
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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. |
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.
1 2 | data <- data_generator(n = 100, p = 50, seed = 123)
data <- data_generator(n = 10, p = 50, case = "asymsparse")
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