criteria.GD: Calculates the values of the Generalised Ds-criterion and its...

In MOODE: Multi-Objective Optimal Design of Experiments

Calculates the values of the Generalised Ds-criterion and its components

Description

This function evaluates the Generalised Ds-criterion \insertCiteGoos2005modelMOODE for given primary and potential model matrices.

Usage

criteria.GD(X1, X2, search.object, eps = 1e-23)

Arguments

X1	The primary model matrix, with the first column containing the labels of treatments, and the second – the intercept term.
X2	The matrix of potential terms, with the first column containing the labels of treatments.
search.object	Object of class mood() specifying experiment parameters.
eps	Computational tolerance, the default value is 10^-23

Value

A list of values: indicator of whether the evaluation was successful ("eval"), Ds-criterion value – intercept excluded ("Ds"), Lack-of-fit criterion value ("LoF"), the bias component value ("bias"), the number of pure error degrees of freedom ("df") and the value of the compound criterion ("compound").

References

\insertAllCited

Examples

#Experiment: one 5-level factor, primary model -- full quadratic, one potential (cubic) term
# setting up the example
ex.mood <- mood(K = 1, Levels = 5, Nruns = 7, criterion.choice = "GDP", 
               kappa = list(kappa.Ds = 1./3, kappa.LoF = 1./3, kappa.bias = 1./3), 
               model_terms = list(primary.model = "second_order", potential.model = "cubic_terms"))
# Generating candidate set: orthonormalised
K <- ex.mood$K
Levels <- ex.mood$Levels 
cand.not.orth <- candidate_set_full(candidate_trt_set(Levels, K), K)
cand.full.orth <- candidate_set_orth(cand.not.orth, ex.mood$primary.terms, ex.mood$potential.terms)
# Choosing a design
index <- c(rep(1, 2), 3, 4, rep(5, 3))
X.primary <- cand.full.orth[index, c(1, match(ex.mood$primary.terms, colnames(cand.full.orth)))]
X.potential <- cand.full.orth[index, 
(c(1, match(ex.mood$potential.terms, colnames(cand.full.orth))))]
# Evaluating a compound GD-criterion
criteria.GD(X1 = X.primary, X2 = X.potential, ex.mood)
# Output: eval = 1, Ds = 0.7334291, LoF = 0.7212544, bias = 1.473138, df = 3, compound = 0.9202307

MOODE documentation built on Aug. 19, 2025, 1:11 a.m.