EvalDesign: Evaluates a design.
In AlgDesign: Algorithmic Experimental Design

eval.design

R Documentation

Evaluates a design.

A design is evaluated.

eval.design(frml,design,confounding=FALSE,variances=TRUE,center=FALSE,X=NULL)

`frml`	The formula used to create the design.
`design`	The design, which may be the design part of the output of optFederov().
`confounding`	If confounding=TRUE, the confounding patterns will be shown.
`variances`	If TRUE, the variances each term will be output.
`center`	If TRUE, numeric variables will be centered before frml is applied.
`X`	X is either the matrix describing the prediction space for I or for G, the the candidate set from which the design was chosen. They are often the same.

`confounding`	A matrix. The columns of which give the regression coefficients of each variable regressed on the others. If `C` is the confounding matrix, then `-ZC` is a matrix of residuals of the variables regressed on the other variables.
`determinant`	`\|M\|^{1/k}`, where `M=Z'Z/N`, and Z is the model expanded `N\times k` design matrix.
`A`	The average coefficient variance: `trace(Mi)/k`, where `Mi` is the inverse of `M`.
`I`	The average prediction variance over X, which can be shown to be `trace((X'X*Mi)/N)`.
`Ge`	The minimax normalized variance over X, expressed as an efficiency with respect to the optimal approximate theory design. It is defined as `k/max(d)`, where `max(d)` is the maximum normalized variance over `X` – i.e. the max of `x'(Mi)x`, over all rows `x'` of `X`.
`Dea`	A lower bound on `D` efficiency for approximate theory designs. It is equal to `exp(1-1/Ge)`.
`diagonality`	The diagonality of the design, excluding the constant, if any. Diagonality is defined as `(\|M_1\|/\prod{diag(M_1)})^{1/k}`, where `M_1` is `M` with first column and row deleted when there is a constant.
`gmean.variances`	The geometric mean of the coefficient variances.