R: Toy data generation

generate.toydata {dmt} | R Documentation

## Toy data generation

### Description

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

### Usage

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

### Arguments

`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 `fit.dependency.model`.

### Details

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.

### Value

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.

### Author(s)

Leo Lahti leo.lahti@iki.fi

### References

See citation("dmt") for references.

### Examples

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

[Package dmt version 0.8.20 Index]