augment_design: Augment Design

In optedr: Calculating Optimal and D-Augmented Designs for Single- and Multi-Factor Models

Augment Design

Description

Augments a design. The user gives an initial design for which he would like to add points and specifies the weight of the new points. Then he is prompted to choose a minimum efficiency. After that, the candidate points region is calculated and the user can choose the points and weights to add.

Usage

augment_design(
  criterion,
  init_design,
  alpha,
  model,
  parameters,
  par_values,
  design_space,
  calc_optimal_design,
  par_int = NA,
  matB = NULL,
  distribution = NA,
  weight_fun = function(x) 1,
  delta_val = NULL,
  new_points = NULL,
  n_lhs = 2000L
)

Arguments

criterion	character variable with the chosen optimality criterion. Can be one of the following: 'D-Optimality' 'Ds-Optimality' 'A-Optimality' 'I-Optimality' 'L-Optimality'
init_design	dataframe with "Point" and "Weight" columns that represents the initial design to augment
alpha	combined weight of the new points
model	formula that represents the model with x as the independent variable
parameters	character vector with the unknown parameters of the model to estimate
par_values	numeric vector with the initial values of the unknown parameters
design_space	numeric vector with the limits of the space of the design
calc_optimal_design	boolean parameter, if TRUE, the optimal design is calculated and efficiencies of the initial and augmented design are given
par_int	optional numeric vector with the index of the parameters of interest for Ds-optimality.
matB	optional matrix of dimensions k x k, for L-optimality.
distribution	character specifying the probability distribution of the response. Can be one of the following: 'Homoscedasticity' 'Gamma', which can be used for exponential or normal heteroscedastic with constant relative error 'Poisson' 'Logistic' 'Log-Normal' (work in progress)
weight_fun	optional one variable function that represents the square of the structure of variance, in case of heteroscedastic variance of the response
delta_val	optional numeric value for the minimum relative efficiency. If NULL (default), the user is prompted interactively. Providing this value enables non-interactive use.
new_points	optional dataframe with Point and Weight columns specifying the points to add. All points must lie within the candidate region determined by delta_val. If NULL (default), the user is prompted interactively.
n_lhs	integer number of LHS points used to visualise the candidate region for multi-factor models with d > 2 (default 2000). Increase for smoother scatter plots.

Value

A dataframe that represents the augmented design

Examples

init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
region <- get_augment_region("D-Optimality", init_des, 0.25,
  y ~ 10^(a - b/(c + x)), c("a", "b", "c"),
  c(8.07131, 1730.63, 233.426), c(1, 100), FALSE, delta_val = 0.85)
new_pts <- data.frame(Point = mean(region$region[1:2]), Weight = 1)
augment_design("D-Optimality", init_des, 0.25, y ~ 10^(a - b/(c + x)),
  c("a", "b", "c"), c(8.07131, 1730.63, 233.426), c(1, 100), FALSE,
  delta_val = 0.85, new_points = new_pts)

optedr documentation built on June 23, 2026, 5:07 p.m.