MOODE (version 1.1.0)

criteria.GDP: Calculates the values of the Generalised DPs-criterion and its components

Description

This function evaluates the Generalised DPs-criterion for given primary and potential model matrices. Components: Ds-, DPs-, LoF(DP)- and Bias(D)-optimality.

Usage

criteria.GDP(X1, X2, search.object, eps = 10^-23)

Value

A list of values: indicator of whether the evaluation was successful ("eval"), Ds-criterion value -- intercept excluded ("Ds"), DPs-criterion value -- intercept excluded ("DPs"), Lack-of-fit(DP) 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").

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

Examples

# Experiment: one 5-level factor, primary model -- full quadratic, X^3 and X^4 potential terms.
ex.mood <- mood(K = 1, Levels = 5, Nruns = 8, criterion.choice = "GDP", 
               kappa = list(kappa.Ds = .25, kappa.LoF = .25, kappa.bias = .25, kappa.DP = .25), 
               model_terms = list(primary.model = "second_order", potential.terms = "x14"))
# Generating candidate sets: primary and full orthonormalised ones
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,rep(4,2),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 GDP-criterion
criteria.GDP(X1 = X.primary, X2 = X.potential, ex.mood)

# Output: eval = 1, Ds = 0.6884783, DP = 4.4538023, LoF = 3.895182, 
# bias = 1.03807, df = 4, compound = 2.465318