sim_mix_networks {nethet} | R Documentation

## sim_mix_networks

### Description

Generate inverse covariances, means, mixing probabilities, and simulate data from resulting mixture model.

### Usage

    sim_mix_networks(n, p, n.comp, sparsity = 0.7, mix.prob = rep(1/n.comp,
      n.comp), Mu = NULL, Sig = NULL, ...)

### Arguments

`n` | Number of data points to simulate.
`p` | Dimensionality of the data.
`n.comp` | Number of components of the mixture model.
`sparsity` | Determines the proportion of non-zero off-diagonal entries.
`mix.prob` | Mixture probabilities for the components; defaults to uniform distribution.
`Mu` | Means for the mixture components, a p by n.comp matrix. If NULL, sampled from a standard Gaussian.
`Sig` | Covariances for the mixture components, a p by p by n.comp array. If NULL, generated using `generate_inv_cov`.
`...` | Further arguments passed to `sim_mix`.

### Details

This function generates n.comp mean vectors from a standard Gaussian and n.comp covariance matrices, with at most (1-sparsity)*p(p-1)/2 non-zero off-diagonal entries, where the non-zero entries are sampled from a beta distribution. Then it uses `sim_mix` to simulate from a mixture model with these means and covariance matrices.

Means Mu and covariance matrices Sig can also be supplied by the user.

### Value

A list with components: `Mu` Means of the mixture components. `Sig` Covariances of the mixture components. `data` Simulated data, a n by p matrix. `S` Component assignments, a vector of length n.

### Examples

    # Generate dataset with 100 samples of dimensionality 30, and 4 components
    test.data = sim_mix_networks(n=100, p=30, n.comp=4)

[Package nethet version 1.26.0 Index]