API Documentation

abstract type AbstractRandomDesign <: AbstractDesign

abstract type AbstractResponseSurfaceDesign <: AbstractDesign

struct IterableFullFactorial <: AbstractFactorialDesign

• matrix::DataFrame

• factors::NamedTuple

• formula::FormulaTerm

struct RandomDesign <: ExperimentalDesign.AbstractRandomDesign

• matrix::DataFrame

• factors::NamedTuple

• formula::FormulaTerm

a_criterion(model_matrix; tolerance) -> Any

Criterion of A-optimality which seeks minimum of $trace((X^T · X)^{-1})$. This criterion results in minimizing the average variance of the estimates of the regression coefficients.

Criterion metric is $\frac{p}{trace((X^T · X)^{-1}) · N}$.

e_criterion(model_matrix; tolerance) -> Any

Criterion of E-optimality maximizes the minimum eigenvalue of the information matrix ($X^T · X$).

Minimization metric is $\frac{\min λ(X^T · X)}{N}$

g_criterion(model_matrix; tolerance) -> Any

Criterion of G-optimality which seeks minimize the maximum entry in the diagonal of $X·(X^T · X)^{-1}·X^T$. This criterion results in minimizing the maximum variance of the predicted values.

Minimization metric is $\frac{N}{\max diag(H)}$ where $H = X·(X^T · X)^{-1}·X^T$.

rd_criterion(model_matrix; tolerance) -> Any

Criterion of rotation distance optimality. Experimental.

t_criterion(model_matrix; tolerance) -> Any

Criterion of T-optimality. This criterion maximize $trace(X^T · X)$.

Minimization metric is $\frac{trace(X^T · X)}{N · p}$.