Description Usage Arguments Details Value Author(s) References Examples
Generate simulated data which follows the distributional assumptions of the model.
1 | generate.toydata(N = 100, zDim = 2, xDim = 3, yDim = 3, marginal.covariances = "full", priors = NULL)
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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 leo.lahti@iki.fi
See citation("dmt") for references.
1 | toy <- generate.toydata(N = 100, zDim = 1, xDim = 3, yDim = 3, marginal.covariances = "full")
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