Aliasing
- class pyoptex.doe.cost_optimal.metric.Aliasing(effects, alias, cov=None, W=None)[source]
The sum of squares criterion for the weighted alias matrix. Computes the mean in case multiple Vinv are provided.
The effects indices from the model matrix are aliased against alias indices from the model matrix.
Attributes
- covfunc(Y, X, Zs, Vinv, costs)
A function computing the covariate parameters and potential extra random effects.
- Wnp.array(2d)
A potential weighting matrix for the elements in aliasing matrix A.
- effectsnp.array(1d)
The indices of the effects in the model matrix to alias from.
- aliasnp.array(1d)
The indices of the effects in the model matrix to alias to.
- __init__(effects, alias, cov=None, W=None)[source]
Creates the metric
Parameters
- effectsnp.array(1d)
The indices of the effects in the model matrix to alias from.
- aliasnp.array(1d)
The indices of the effects in the model matrix to alias to.
- covfunc(Y, X, Zs, Vinv, costs)
The covariance function
- Wnp.array(1d)
The weights for the aliasing matrix.
Methods
Aliasing.call(Y, X, Zs, Vinv, costs)Computes the aliasing criterion for a given design.
Aliasing.init(params)Initializes the metric before optimization.