no_cov

pyoptex.doe.cost_optimal.cov.no_cov(Y, X, Zs, Vinv, costs, random=False)

Function to indicate no covariate is added.

Parameters

Ynp.array(2d)

The design matrix

Xnp.array(2d)

The model matrix

Zslist(np.array(1d))

The grouping matrices

Vinvnp.array(3d)

The inverses of the multiple covariance matrices for each set of a-priori variance ratios.

costslist(np.array(1d), float, np.array(1d))

The list of different costs.

randombool

Whether to add covariates at random or predetermined. The random aspect is used for sampling random points in the design space.

Returns

Ynp.array(2d)

The updated design matrix with covariates.

Xnp.array(2d)

The updated model matrix with covariates.

Zslist(np.array(1d))

The updated grouping matrices with added random covariate effects.

Vinv = np.array(3d)

The updated inverses of the covariance matrices with the added random covariate effects.