generate.toydata: Toy data generation

Description Usage Arguments Details Value Author(s) References Examples

Description

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

Usage

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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 [email protected]

References

See citation("dmt") for references.

Examples

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

dmt documentation built on May 1, 2019, 8:12 p.m.