Description Usage Arguments Details Value Author(s) References Examples

Generate simulated data which follows the distributional assumptions of the model.

1 2 | ```
generate.toydata(N = 100, zDim = 2, xDim = 3, yDim = 3,
marginal.covariances = "full", priors = NULL)
``` |

`N` |
Sample size. |

`zDim` |
Dimensionality of the latent variable. |

`xDim` |
Dimensionality of X data set. |

`yDim` |
Dimensionality of Y data set. |

`marginal.covariances` |
"full": full covariance matrices for marginal noise (assumed by pCCA); "diagonal": diagonal covariances for marginal noise (pFA); "isotropic": isotropic covariances (pPCA). |

`priors` |
Set priors for toydata generation. Use as in |

Assuming normally distributed latent variables for shared component Z, and data-specific components Zx, Zy. These follow standard multivariate normal distribution N(0, I). The observations X and Y are obtained as X = Wx*Z + Bx*Zx, Y = Wy*Z + By*Zy.

List with the following components:

`Z, Zx, Zy ` |
Shared and data-set specific latent variables. |

`Wx, Wy, Bx, By ` |
Transformation matrices. |

`X, Y ` |
Data sets. |

Leo Lahti [email protected]

See citation("dmt") for references.

1 2 3 | ```
toy <- generate.toydata(N = 100,
zDim = 1, xDim = 3, yDim = 3,
marginal.covariances = "full")
``` |

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