Help for package optedr

Title:

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

Version:

3.0.0

Description:

Calculates D-, Ds-, A-, I- and L-optimal designs, weighted combinations of these via a Compound criterion, and KL-optimal designs for model discrimination, for non-linear single- and multi-factor models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Multi-factor models use design variables x1, x2, … with a named-list design space; single-factor models remain backward compatible. Compares designs via their efficiency, augments any design with a controlled efficiency loss, and provides efficient rounding functions to convert approximate designs to exact ones.

License:

GPL-3

Encoding:

UTF-8

URL:

https://github.com/kezrael/optedr, https://github.com/Kezrael/optedr

BugReports:

https://github.com/Kezrael/optedr/issues

RoxygenNote:

7.3.1

VignetteBuilder:

knitr

Suggests:

testthat (≥ 3.0.0), mockery, knitr, rmarkdown, markdown, DT, shinydashboard, shinyalert, plotly, hrbrthemes, shinyjs, orthopolynom, magrittr, tidyverse, nleqslv

Imports:

ggplot2, purrr, rlang, crayon, cli, shiny, utils

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-06-23 09:01:24 UTC; Carlos

Author:

Carlos de la Calle-Arroyo

[aut, cre], Jesús López-Fidalgo

[aut], Licesio J. Rodríguez-Aragón

[aut]

Maintainer:

Carlos de la Calle-Arroyo <carlos.calle.arroyo@gmail.com>

Repository:

CRAN

Date/Publication:

2026-06-23 10:10:02 UTC

Cocktail Algorithm implementation for Compound Optimality

Description

Cocktail Algorithm implementation for Compound Optimality

Usage

CWFMult(
  init_design,
  grad,
  compound_specs,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2,
  max_iter
)

Arguments

init_design

optional dataframe with the initial design for the algorithm.

grad

function of partial derivatives of the model.

compound_specs

preprocessed list of per-criterion specifications.

design_space

named list with bounds for each design variable.

grid.length

numeric value for the sensitivity search grid / LHS size.

join_thresh

numeric value for the merge heuristic.

delete_thresh

numeric value for the weight deletion threshold.

delta_weights

numeric value in (0, 1), parameter of the algorithm.

tol

numeric value for convergence of the weight loop.

tol2

numeric value for the outer stop condition.

max_iter

maximum number of outer iterations.

Value

An object of class optdes.

Cocktail Algorithm implementation for D-Optimality

Description

Cocktail Algorithm implementation for D-Optimality

Usage

DWFMult(
  init_design,
  grad,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  k,
  delta_weights,
  tol,
  tol2,
  max_iter
)

Arguments

init_design

optional dataframe with the initial design for the algorithm.

grad

function of partial derivatives of the model.

design_space

named list with bounds for each design variable.

grid.length

numeric value for the sensitivity search grid / LHS size.

join_thresh

numeric value for the merge heuristic.

delete_thresh

numeric value for the weight deletion threshold.

k

number of unknown parameters of the model.

delta_weights

numeric value in (0, 1), parameter of the algorithm.

tol

numeric value for convergence of the weight loop.

tol2

numeric value for the outer stop condition.

max_iter

maximum number of outer iterations.

Value

An object of class optdes.

Cocktail Algorithm implementation for Ds-Optimality

Description

Cocktail Algorithm implementation for Ds-Optimality

Usage

DsWFMult(
  init_design,
  grad,
  par_int,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2,
  max_iter
)

Arguments

init_design

optional dataframe with the initial design for the algorithm.

grad

function of partial derivatives of the model.

par_int

numeric vector with the index of the parameters of interest (Ds-Optimality).

design_space

named list with bounds for each design variable.

grid.length

numeric value for the sensitivity search grid / LHS size.

join_thresh

numeric value for the merge heuristic.

delete_thresh

numeric value for the weight deletion threshold.

delta_weights

numeric value in (0, 1), parameter of the algorithm.

tol

numeric value for convergence of the weight loop.

tol2

numeric value for the outer stop condition.

max_iter

maximum number of outer iterations.

Value

An object of class optdes.

Cocktail Algorithm implementation for L-, I- and A-Optimality

Description

Cocktail Algorithm implementation for L-, I- and A-Optimality

Usage

IWFMult(
  init_design,
  grad,
  matB,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2,
  criterion,
  max_iter
)

Arguments

init_design

optional dataframe with the initial design for the algorithm.

grad

function of partial derivatives of the model.

matB

optional matrix for L-optimality.

design_space

named list with bounds for each design variable.

grid.length

numeric value for the sensitivity search grid / LHS size.

join_thresh

numeric value for the merge heuristic.

delete_thresh

numeric value for the weight deletion threshold.

delta_weights

numeric value in (0, 1), parameter of the algorithm.

tol

numeric value for convergence of the weight loop.

tol2

numeric value for the outer stop condition.

criterion

character variable with the chosen optimality criterion.

max_iter

maximum number of outer iterations.

Value

An object of class optdes.

Cocktail Algorithm for KL-Optimality

Description

Cocktail Algorithm for KL-Optimality

Usage

KLWFMult(
  init_design,
  kl_fun,
  beta2_init,
  lower,
  upper,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2,
  max_iter,
  kl_meta = list(type = "kl_fun")
)

Arguments

init_design

optional dataframe with the initial design for the algorithm.

kl_fun

function(x, beta2); returns the KL divergence at design point x given rival parameters beta2.

beta2_init

numeric vector of initial rival parameter values.

lower

lower bounds for rival parameters in the inner optimisation.

upper

upper bounds for rival parameters in the inner optimisation.

design_space

named list with bounds for each design variable.

grid.length

numeric value for the sensitivity search grid / LHS size.

join_thresh

numeric value for the merge heuristic.

delete_thresh

numeric value for the weight deletion threshold.

delta_weights

numeric value in (0, 1), parameter of the algorithm.

tol

numeric value for convergence of the weight loop.

tol2

numeric value for the outer stop condition.

max_iter

maximum number of outer iterations.

kl_meta

list with summary metadata (type, and optionally family and phi for the standard path).

Value

An object of class optdes.

Master function for the cocktail algorithm, that calls the appropriate one given the criterion.

Description

Depending on the criterion the cocktail algorithm for the chosen criterion is called, and the necessary parameters for the functions are given from the user input.

Usage

WFMult(
  init_design,
  grad,
  criterion,
  par_int = NA,
  matB = NA,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  k,
  delta_weights,
  tol,
  tol2,
  max_iter,
  compound = NULL,
  kl_spec = NULL
)

Arguments

init_design

optional dataframe with the initial design for the algorithm.

grad

function of partial derivatives of the model.

criterion

character variable with the chosen optimality criterion.

par_int

numeric vector with the index of the parameters of interest (Ds-Optimality).

matB

optional matrix for L-optimality.

design_space

named list with bounds for each design variable.

grid.length

numeric value for the sensitivity search grid / LHS size.

join_thresh

numeric value for the merge heuristic.

delete_thresh

numeric value for the weight deletion threshold.

k

number of unknown parameters of the model.

delta_weights

numeric value in (0, 1), parameter of the algorithm.

tol

numeric value for convergence of the weight loop.

tol2

numeric value for the outer stop condition.

max_iter

maximum number of outer iterations.

compound

preprocessed list of compound criterion specifications (internal use).

kl_spec

preprocessed list of KL-Optimality specifications (internal use).

Value

An object of class optdes.

Add two designs

Description

Add two designs

Usage

add_design(design_1, design_2, alpha)

Arguments

design_1

A dataframe with 'Point' and 'Weight' as columns that represent the first design to add

design_2

A dataframe with 'Point' and 'Weight' as columns that represent the second design to add

alpha

Weight of the first design

Value

A design as a dataframe with the weighted addition of the two designs

Update design given crosspoints and alpha

Description

Given a set of points, a weight and the design, the function adds these points to the new design with uniform weight, and combined weight alpha

Usage

add_points(points, alpha, design)

Arguments

points

Points to be added to the design

alpha

Combined weight of the new points to be added to the design

design

A design as a dataframe with "Point" and "Weight" columns

Value

A design as a dataframe with "Point" and "Weight" columns that is the addition of the design and the new points

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)

Check Inputs

Description

Function to check that the inputs given to the function opt_des are correct. If not, throws the correspondent error message.

Usage

check_inputs(
  criterion,
  model,
  parameters,
  par_values,
  design_space,
  init_design,
  join_thresh,
  delete_thresh,
  delta,
  tol,
  tol2,
  par_int,
  matB,
  reg_int,
  weight_fun
)

Arguments

criterion

character variable with the chosen optimality criterion.

model

formula describing the model. For single-factor models use x as the design variable; for multi-factor models use x1, x2, etc.

parameters

character vector with the parameter names.

par_values

numeric vector with the nominal parameter values.

design_space

For single-factor models: numeric vector c(min, max). For multi-factor models: named list list(x1 = c(min, max), x2 = c(min, max), ...).

init_design

optional dataframe with the initial design. For single-factor models use columns Point and Weight; for multi-factor models use one column per design variable plus Weight.

join_thresh

optional numeric threshold for merging nearby design points.

delete_thresh

optional numeric minimum weight to keep a support point.

delta

optional numeric in (0, 1), damping parameter of the algorithm.

tol

optional numeric for convergence of the weight loop.

tol2

optional numeric for the outer stop condition.

par_int

optional numeric vector of parameter indices for Ds-optimality.

matB

optional k x k matrix for L-optimality.

reg_int

optional bounds for the I-optimality integration region. Single-factor: c(min, max). Multi-factor: named list matching design_space.

weight_fun

optional variance-structure weight function.

Combinatorial round

Description

Given an approximate design and a number of points, computes all the possible combinations of roundings of each point to the nearest integer, keeps the ones that amount to the requested number of points, and returns the one with the best value for the criterion function.

The search is exhaustive and requires 2^k evaluations where k is the number of support points. For designs with more than max_support support points the function requests confirmation (interactive sessions) or stops with an informative error (non-interactive sessions), unless ask = FALSE.

Usage

combinatorial_round(
  design,
  n,
  criterion = NULL,
  model = NULL,
  parameters = NULL,
  par_values = NULL,
  weight_fun = function(x) 1,
  par_int = NULL,
  reg_int = NULL,
  matB = NULL,
  max_support = 15L,
  ask = TRUE
)

Arguments

design

either a dataframe with the design to round, or an object of class "optdes". If a dataframe, the criterion, model and parameters must be specified. It must have a Weight column and one or more design-variable columns (Point for single-factor, x1, x2, ... for multi-factor).

n

integer with the desired number of points of the resulting design.

criterion

character variable with the chosen optimality criterion. Can be one of the following:

'D-Optimality'
'Ds-Optimality'
'A-Optimality'
'I-Optimality'
'L-Optimality'

model

formula describing the model. Must use x (single-factor) or x1, x2, ... (multi-factor) as design variables.

parameters

character vector with the parameters of the models, as written in the formula.

par_values

numeric vector with the parameters nominal values, in the same order as given in parameters.

weight_fun

optional one variable function that represents the square of the structure of variance, in case of heteroscedastic variance of the response.

par_int

optional numeric vector with the index of the parameters of interest for Ds-optimality.

reg_int

optional numeric vector with the ranges of integration, for I-optimality.

matB

optional matrix of dimensions k x k, for L-optimality.

max_support

integer. Number of support points above which the function triggers the confirmation mechanism. Default is 15 (2^{15} \approx 32\,000 combinations).

ask

logical. If TRUE (default) and the design exceeds max_support: in an interactive session the user is prompted; in a non-interactive session an error is raised. Set ask = FALSE to skip confirmation in scripts or pipelines (a message is still emitted).

Value

A data.frame with the rounded design to n number of points, or NULL invisibly if the user declines the confirmation prompt.

Examples

aprox_design <- opt_des("D-Optimality", y ~ a * exp(-b / x), c("a", "b"), c(1, 1500), c(212, 422))
combinatorial_round(aprox_design, 27)

Master function for the criterion function

Description

Depending on the criterion input, the function returns the output of the corresponding criterion function given the information matrix.

Usage

crit(criterion, M, k = 0, par_int = c(1), matB = NA)

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'

M

information matrix for which the criterion value wants to be calculated.

k

numeric variable with the number of parameters of the model. Taken from the number of rows of the matrix if omitted.

par_int

numeric vector with the index of the parameters of interest of the model. Only for "Ds-Optimality".

matB

optional matrix of dimensions k x k, for I- and L-optimality.

Value

Numeric value of the optimality criterion for the information matrix.

Calculate crosspoints

Description

Given the parameters for augmenting a design, this function calculates the crosspoints in the efficiency function that delimit the candidate points region

Usage

crosspoints(val, sens, gridlength, tol, xmin, xmax)

Arguments

val

Efficiency value to solve in the curve relationing the space of the design and efficiency of new design

sens

Sensitivity function of the design for the model

gridlength

Number of points in the initial grid used to bracket roots

tol

Tolerance for root refinement passed to uniroot

xmin

Minimum of the space of the design

xmax

Maximum of the space of the design

Value

A numeric vector of crosspoints that define the candidate points region

D-Augment Design

Description

D-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

daugment_design(
  init_design,
  alpha,
  model,
  parameters,
  par_values,
  design_space,
  calc_optimal_design,
  weight_fun = function(x) 1,
  delta_val = NULL,
  new_points = NULL
)

Arguments

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

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

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)

Criterion function for D-Optimality

Description

Calculates the value of the D-Optimality criterion function, which follows the expression:

\phi_D = \left(\frac{1}{|M|}\right)^{1/k}

Usage

dcrit(M, k)

Arguments

M

information matrix for which the criterion value wants to be calculated.

k

numeric variable with the number of parameters of the model. Taken from the number of rows of the matrix if omitted.

Value

numeric value of the D-optimality criterion for the information matrix.

Remove low weight points

Description

Removes the points of a design with a weight lower than a threshold, delta, and distributes that weights proportionally to the rest of the points.

Usage

delete_points(design, delta)

Arguments

design

The design from which to remove points as a dataframe with two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

delta

The threshold from which the points with such a weight or lower will be removed.

Value

The design without the removed points.

Efficiency between optimal design and a user given design

Description

Takes an optimal design provided from the function opt_des and a user given design and compares their efficiency

Usage

design_efficiency(design, opt_des_obj)

Arguments

design

dataframe that represents the design. Must have two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

opt_des_obj

an object given by the function opt_des.

Value

The efficiency as a value between 0 and 1

Examples

result <- opt_des("D-Optimality", y ~ a * exp(-b / x), c("a", "b"), c(1, 1500), c(212, 422))
design <- data.frame("Point" = c(220, 240, 400), "Weight" = c(1 / 3, 1 / 3, 1 / 3))
design_efficiency(design, result)

Detect design variables from a model formula

Description

Inspects the formula to identify which symbols are design variables (as opposed to parameters or the response). Enforces the naming convention: "x" for single-factor models and "x1", "x2", ... for multi-factor models.

Usage

detect_design_vars(model, parameters)

Arguments

model

A formula describing the model.

parameters

Character vector of parameter names (excluded from design variables).

Value

Character vector of design variable names, sorted in numerical order for multi-factor models (e.g. c("x1", "x2", "x10")).

Ds-Augment Design

Description

Ds-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

dsaugment_design(
  init_design,
  alpha,
  model,
  parameters,
  par_values,
  par_int,
  design_space,
  calc_optimal_design,
  weight_fun = function(x) 1,
  delta_val = NULL,
  new_points = NULL
)

Arguments

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

par_int

optional numeric vector with the index of the parameters of interest for Ds-optimality.

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

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

Value

A dataframe that represents the Ds-augmented design

Examples

## Not run: 
init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
augment_design("Ds-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), par_int = c(1), TRUE)

## End(Not run)

Criterion function for Ds-Optimality

Description

Calculates the value of the Ds-Optimality criterion function, which follows the expression:

\phi_{Ds} = \left(\frac{|M_{22}|}{|M|}\right)^{1/s}

Usage

dscrit(M, par_int)

Arguments

M

information matrix for which the criterion value wants to be calculated.

par_int

numeric vector with the index of the parameters of interest of the model.

Value

Numeric value of the Ds-optimality criterion for the information matrix.

Sensitivity function for D-Optimality

Description

Calculates the sensitivity function from the gradient vector and the Identity Matrix.

Usage

dsens(grad, M)

Arguments

grad

A function in a single variable that returns the partial derivatives vector of the model.

M

Information Matrix for the sensitivity function.

Value

The sensitivity function as a matrix of single variable.

Sensitivity function for Ds-Optimality

Description

Calculates the sensitivity function from the gradient vector, the Identity Matrix and the parameters of interest.

Usage

dssens(grad, M, par_int)

Arguments

grad

A function in a single variable that returns the partial derivatives vector of the model.

M

Information Matrix for the sensitivity function.

par_int

Numeric vector of the indexes of the parameters of interest for Ds-Optimality.

Value

The sensitivity function as a matrix of single variable.

Efficiency between two Information Matrices

Description

Efficiency between two Information Matrices

Usage

eff(criterion, mat1, mat2, k = 0, intPars = c(1), matB = NA)

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'

mat1

first information matrix, for the numerator.

mat2

second information matrix, for the denominator.

k

number of parameters of the model. Taken from the number of rows of the matrix if omitted.

intPars

numeric vector with the index of the parameters of interest of the model. Only for "Ds-Optimality".

matB

matrix of the integral of the information matrix over the interest region. Only for "I-Optimality".

Value

Efficiency of first design with respect to the second design, as a decimal number.

Efficient Round

Description

Takes an approximate design, and a number of points and converts the design to an approximate design. It uses the multiplier (n - l/2) and evens the total number of observations afterwards.

Usage

efficient_round(design, n, tol = 1e-05, seed = NULL)

Arguments

design

a dataframe with a Weight column and one or more design-variable columns (Point for single-factor designs, x1, x2, ... for multi-factor designs).

n

an integer that represents the desired number of observations of the exact design

tol

optional parameter for the consideration of an integer in the rounding process

seed

optional integer seed for reproducibility. When the rounded weights sum to less than n, a random tie-breaking step is used; supplying seed makes that step deterministic by calling set.seed(seed) immediately before it. NULL (default) leaves the global RNG state unchanged.

Value

a data.frame with the same columns as design, with Weight replaced by integer observation counts summing to n.

Examples

design_test <- data.frame("Point" = seq(1, 5, length.out = 7),
         "Weight" = c(0.1, 0.0001, 0.2, 0.134, 0.073, 0.2111, 0.2818))

efficient_round(design_test, 20)

exact_design <- efficient_round(design_test, 21)
aprox_design <- exact_design
aprox_design$Weight <- aprox_design$Weight/sum(aprox_design$Weight)

# Reproducible tie-breaking
efficient_round(design_test, 20, seed = 42)

Find Maximum

Description

Searches the location of the maximum of a sensitivity function over the design space. For single-factor models the search uses a regular grid followed by direct selection. For multi-factor models a Latin Hypercube Sample is evaluated and then refined with L-BFGS-B local optimisation from the n_starts best candidate points.

Usage

findmax(sens, design_space, grid.length, n_starts = 5L)

Arguments

sens

Sensitivity function (scalar for 1D, named numeric vector for multi-factor).

design_space

Numeric vector c(min, max) or named list.

grid.length

Number of grid / LHS points for the initial sweep.

n_starts

Number of local-optimisation restarts (multi-factor only).

Value

The design point (scalar or named numeric vector) at which sens is maximised.

Find Maximum Value

Description

Searches the maximum of a function over a grid on a given interval or design space.

Usage

findmaxval(sens, design_space, grid.length)

Arguments

sens

A numeric function to evaluate (scalar argument for 1D, named numeric vector for multi-factor).

design_space

Numeric vector c(min, max) for single-factor models, or a named list list(x1 = c(min, max), ...) for multi-factor models.

grid.length

Number of grid points (1D) or LHS samples (multi-factor).

Value

The value of the maximum

Find Minimum Value

Description

Searches the minimum of a function over a grid on the design space.

Usage

findminval(sens, design_space, grid.length)

Arguments

sens

A numeric function to evaluate (scalar for 1D, named numeric vector for multi-factor).

design_space

Numeric vector c(min, max) for single-factor models, or a named list list(x1 = c(min, max), ...) for multi-factor models.

grid.length

Number of grid points (1D) or LHS samples (multi-factor).

Value

The value of the minimum

Give effective limits to candidate points region

Description

Given the start of the candidates points region, the parity of the crosspoints and the boundaries of the space of the design returns the effective limits of the candidate points region. Those points, taken in pairs from the first to the last delimit the region.

Usage

getCross2(cross, min, max, start, par)

Arguments

cross

Vector of crosspoints in the sensitivity function given an efficiency and weight

min

Minimum of the space of the design

max

Maximum of the space of the design

start

Boolean that gives the effective start of the candidate points region

par

Boolean with the parity of the region

Value

Vector of effective limits of the candidate points region. Taken in pairs from the beginning delimit the region.

Parity of the crosspoints

Description

Determines if the number of crosspoints is even or odd given the vector of crosspoints

Usage

getPar(cross)

Arguments

cross

Vector of crosspoints in the sensitivity function given an efficiency and weight

Value

True if the number of crosspoints is even, false otherwise

Find where the candidate points region starts

Description

Given the crosspoints and the sensitivity function, this function finds where the candidate points region starts, either on the extreme of the space of the design or the first crosspoints

Usage

getStart(cross, min, max, val, sens_opt)

Arguments

cross

Vector of crosspoints in the sensitivity function given an efficiency and weight

min

Minimum of the space of the design

max

Maximum of the space of the design

val

Value of the sensitivity function at the crosspoints

sens_opt

Sensitivity function

Value

True if the candidate points region starts on the minimum, False otherwise

Get Augment Regions

Description

Given a model and criterion, calculates the candidate points region. 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.

Usage

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

Arguments

criterion

character 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 represent 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.

n_lhs

integer number of LHS points used to visualise and sample the candidate region for multi-factor models with d > 2 (default 2000). Increase for smoother scatter plots.

Value

A vector of the points limiting the candidate points region

Examples


init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
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)

Get D-augment region

Description

Given a model, calculates the candidate points region for D-Optimality. 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.

Usage

get_daugment_region(
  init_design,
  alpha,
  model,
  parameters,
  par_values,
  design_space,
  calc_optimal_design,
  weight_fun = function(x) 1,
  delta_val = NULL
)

Arguments

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 represent 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

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.

Value

A vector of the points limiting the candidate points region

Examples


init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
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)

Get Ds-augment region

Description

Given a model, calculates the candidate points region for Ds-Optimality. 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.

Usage

get_dsaugment_region(
  init_design,
  alpha,
  model,
  parameters,
  par_values,
  par_int,
  design_space,
  calc_optimal_design,
  weight_fun = function(x) 1,
  delta_val = NULL
)

Arguments

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 represent 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

par_int

optional numeric vector with the index of the parameters of interest for Ds-optimality.

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

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.

Value

A vector of the points limiting the candidate points region

Examples


init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
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)

Get L-augment region

Description

Given a model, calculates the candidate points region for L-Optimality. 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.

Usage

get_laugment_region(
  init_design,
  alpha,
  model,
  parameters,
  par_values,
  design_space,
  calc_optimal_design,
  matB,
  weight_fun = function(x) 1,
  delta_val = NULL
)

Arguments

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 represent 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

matB

optional matrix of dimensions k x k, for L-optimality.

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.

Value

A vector of the points limiting the candidate points region

Examples


init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
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)

Gradient function

Description

Calculates the gradient function of a model with respect to the parameters, char_vars, evaluates it at the provided values and returns the result as a function of the variable x.

Usage

gradient(model, char_vars, values, weight_fun = function(x) 1)

Arguments

model

formula describing the model, which must contain only x, the parameters defined in char_vars and the numerical operators.

char_vars

character vector of the parameters of the model.

values

numeric vector with the nominal values of the parameters in char_vars.

weight_fun

optional function variable that represents the square of the structure of variance, in case of heteroscedastic variance of the response

Value

A function depending on x that's the gradient of the model with respect to char_vars

Gradient function for a subset of variables

Description

Calculates the gradient function of a model with respect to a subset of the parameters given in par_int, char_vars, evaluates it at the provided values and returns the result as a function of the variable x.

Usage

gradient22(model, char_vars, values, par_int, weight_fun = function(x) 1)

Arguments

model

formula describing the model, which must contain only x, the parameters defined in char_vars and the numerical operators.

char_vars

character vector of the parameters of the model.

values

numeric vector with the nominal values of the parameters in char_vars.

par_int

vector of indexes indicating the subset of variables to omit in the calculation of the gradient.

weight_fun

optional one variable function that represents the square of the structure of variance, in case of heteroscedastic variance of the response

Value

A function depending on x that's the gradient of the model with respect to char_vars

Criterion function for I-Optimality and L-Optimality

Description

Calculates the value of the I-Optimality criterion function, which follows the expression:

\phi_I = Tr(M^{-1} \cdot B)

Usage

icrit(M, matB)

Arguments

M

information matrix for which the criterion value wants to be calculated.

matB

matrix of the integral of the information matrix over the interest region. Identity matrix for A-Optimality.

Value

Numeric value of the I-optimality criterion for the information matrix.

Information Matrix

Description

Given the gradient vector of a model in a single variable model and a design, calculates the information matrix.

Usage

inf_mat(grad, design)

Arguments

grad

A function in a single variable that returns the partial derivatives vector of the model.

design

A dataframe that represents the design. Must have two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

Value

The information matrix of the design, a k\times k matrix where k is the length of the gradient.

Integrate IM

Description

Integrates the information matrix over the region of interest to calculate matrix B to be used in I-Optimality calculation.

Usage

integrate_reg_int(grad, k, reg_int)

Arguments

grad

function of partial derivatives of the model.

k

number of unknown parameters of the model.

reg_int

optional numeric vector of two components with the bounds of the interest region for I-Optimality.

Value

The integrated information matrix.

Sensitivity function for I-Optimality

Description

Calculates the sensitivity function from the gradient vector, the Information Matrix and the integral of the one-point Identity Matrix over the interest region. If instead the identity matrix is used, it can be used for A-Optimality.

Usage

isens(grad, M, matB)

Arguments

grad

A function in a single variable that returns the partial derivatives vector of the model.

M

Information Matrix for the sensitivity function.

matB

Matrix resulting from the integration of the one-point Information Matrix along the interest region or lineal matrix for L-Optimality.

Value

The sensitivity function as a matrix of single variable.

L-Augment Design

Description

L-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

laugment_design(
  init_design,
  alpha,
  model,
  parameters,
  par_values,
  design_space,
  calc_optimal_design,
  matB,
  weight_fun = function(x) 1,
  delta_val = NULL,
  new_points = NULL
)

Arguments

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

matB

optional matrix of dimensions k x k, for L-optimality.

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

Value

A dataframe that represents the L-augmented design

Examples

## Not run: 
init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
augment_design("I-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), TRUE)

## End(Not run)

GLM family specification for KL-Optimality

Description

Returns a GLM family object for use with criterion = "KL-Optimality". The family encodes the cumulant generating function and canonical link needed to compute the Kullback-Leibler divergence between two distributions.

Usage

make_glm_family(name)

Arguments

name

character; one of "Normal", "Poisson", "Binomial" or "Gamma".

Value

A named list with elements b, b_prime, b_dbl, link, link_inv and name.

Examples

make_glm_family("Poisson")
make_glm_family("Normal")

Build a KL-divergence point function for use with opt_des()

Description

Returns a function(x, beta2) computing the point KL divergence at design point x given rival parameters beta2. The result is passed to opt_des via the kl_fun argument, allowing discrimination between models with different family, dispersion, or mean structure without having to derive the formula manually.

Supported family pairs:

Same family and same phi: any of "Normal", "Poisson", "Binomial", "Gamma". Uses the standard exponential-family cumulant formula.
"Normal" vs "Normal" with phi1 != phi2: KL = \frac{1}{2}\bigl[\log(\phi_2/\phi_1) + \phi_1/\phi_2 + (\mu_1-\mu_2)^2/\phi_2 - 1\bigr].
"Gamma" vs "Gamma" with different shape (k_i = 1/\phi_i): closed form involving digamma and lgamma.

For other cross-family pairs provide kl_fun directly.

Usage

make_kl_fun(
  family1,
  model1,
  params1,
  par_values1,
  family2 = family1,
  model2 = model1,
  params2 = params1,
  phi1 = 1,
  phi2 = phi1
)

Arguments

family1

character; reference distribution ("Normal", "Poisson", "Binomial" or "Gamma").

model1

formula; reference model mean function.

params1

character vector; parameter names in model1.

par_values1

numeric vector; nominal values for the reference parameters.

family2

character; rival distribution (default: same as family1).

model2

formula; rival model mean function (default: same as model1).

params2

character vector; rival parameter names (optimised internally). Default: same as params1.

phi1

positive numeric; dispersion of the reference (Normal: variance; Gamma: 1/\text{shape}).

phi2

positive numeric; dispersion of the rival (default: same as phi1).

Value

A function function(x, beta2) giving the point KL divergence. Works for both 1-D (x scalar) and multi-factor designs (x named numeric vector).

Examples

# Same family (Normal), different model structures
kl_fn <- make_kl_fun(
  "Normal",
  model1 = y ~ Vmax * x / (Km + x), params1 = c("Vmax", "Km"),
  par_values1 = c(2, 1),
  model2 = y ~ a * x, params2 = "a"
)
kl_fn(x = 1, beta2 = 0.5)


# Normal vs Normal with different variance (phi2 = 4)
kl_fn2 <- make_kl_fun(
  "Normal",
  model1 = y ~ a * exp(-b * x), params1 = c("a", "b"),
  par_values1 = c(1, 0.5), phi1 = 1,
  family2 = "Normal",
  model2 = y ~ c * exp(-d * x), params2 = c("c", "d"), phi2 = 4
)
opt_des("KL-Optimality",
        model = y ~ a * exp(-b * x), parameters = c("a", "b"),
        par_values = c(1, 0.5), design_space = c(0, 4),
        kl_fun = kl_fn2, rival_pars = c(1, 1),
        rival_lower = c(0.5, 0.8), rival_upper = c(2, 1.5))

Calculates the optimal design for a specified criterion

Description

The opt_des function calculates the optimal design for an optimality criterion and a model input from the user. Supports single-factor models (design variable named x) and multi-factor models (design variables named x1, x2, ..., detected automatically from the formula).

Usage

opt_des(
  criterion,
  model,
  parameters,
  par_values = c(1),
  design_space,
  init_design = NULL,
  join_thresh = -1,
  delete_thresh = 0.02,
  delta = 1/2,
  tol = 1e-05,
  tol2 = 1e-05,
  par_int = NULL,
  matB = NULL,
  reg_int = NULL,
  max_iter = 21L,
  distribution = NA,
  weight_fun = function(x) 1,
  compound = NULL,
  rival_model = NULL,
  rival_params = NULL,
  rival_pars = NULL,
  family = "Normal",
  phi = 1,
  rival_lower = NULL,
  rival_upper = NULL,
  kl_fun = NULL
)

Arguments

criterion

character variable with the chosen optimality criterion.

model

formula describing the model. For single-factor models use x as the design variable; for multi-factor models use x1, x2, etc.

parameters

character vector with the parameter names.

par_values

numeric vector with the nominal parameter values.

design_space

For single-factor models: numeric vector c(min, max). For multi-factor models: named list list(x1 = c(min, max), x2 = c(min, max), ...).

init_design

optional dataframe with the initial design. For single-factor models use columns Point and Weight; for multi-factor models use one column per design variable plus Weight.

join_thresh

optional numeric threshold for merging nearby design points.

delete_thresh

optional numeric minimum weight to keep a support point.

delta

optional numeric in (0, 1), damping parameter of the algorithm.

tol

optional numeric for convergence of the weight loop.

tol2

optional numeric for the outer stop condition.

par_int

optional numeric vector of parameter indices for Ds-optimality.

matB

optional k x k matrix for L-optimality.

reg_int

optional bounds for the I-optimality integration region. Single-factor: c(min, max). Multi-factor: named list matching design_space.

max_iter

optional integer maximum number of outer cocktail iterations.

distribution

character variable specifying the response distribution.

weight_fun

optional variance-structure weight function.

compound

optional list of criterion specifications for criterion = "Compound". Each element must be a named list with at least criterion (character) and weight (positive numeric). Additional fields per sub-criterion: par_int (Ds-Optimality), reg_int (I-Optimality), matB (L-Optimality). Weights are normalised to sum to 1. Example: list(list(criterion="D-Optimality", weight=0.7), list(criterion="I-Optimality", weight=0.3, reg_int=c(380,422))).

rival_model

optional formula for the rival model used with criterion = "KL-Optimality". Defaults to model (same structure, rival parameters explored by inner optimisation).

rival_params

optional character vector of rival model parameter names. Defaults to parameters.

rival_pars

optional numeric vector of initial rival parameter values for the inner optimisation. Defaults to par_values.

family

character; GLM family for criterion = "KL-Optimality". One of "Normal", "Poisson", "Binomial", "Gamma". Default "Normal".

phi

positive numeric dispersion parameter for KL-Optimality. Default 1.

rival_lower

optional numeric vector of lower bounds for rival parameters in the inner optimisation. Defaults to -Inf for each parameter.

rival_upper

optional numeric vector of upper bounds for rival parameters in the inner optimisation. Defaults to Inf for each parameter.

kl_fun

optional user-supplied function(x, beta2) giving the point KL divergence at design point x for rival parameters beta2, as built by make_kl_fun. When provided, bypasses the family/rival_model path entirely; rival_pars must still be supplied for the inner optimisation.

Value

a list of class optdes with components optdes, convergence, sens, criterion, crit_value, and atwood.

Examples

# Single-factor (backward compatible)
opt_des("D-Optimality", y ~ a * exp(-b / x), c("a", "b"), c(1, 1500), c(212, 422))


# Two-factor Michaelis-Menten bisubstrate model
opt_des("D-Optimality",
        y ~ Vmax * x1 * x2 / ((K1 + x1) * (K2 + x2)),
        c("Vmax", "K1", "K2"), c(1, 1, 1),
        list(x1 = c(0.1, 10), x2 = c(0.1, 10)))

# Compound D+I (70% D, 30% I) for Antoine equation
opt_des("Compound",
        y ~ 10^(a - b/(c + x)), c("a","b","c"),
        c(8.07131, 1730.63, 233.426), c(1, 100),
        compound = list(
          list(criterion = "D-Optimality", weight = 0.7),
          list(criterion = "I-Optimality", weight = 0.3, reg_int = c(60, 100))
        ))

# KL-Optimality: discriminate quadratic from linear mean model (Normal)
opt_des("KL-Optimality",
        model        = y ~ a * x^2,
        parameters   = c("a"),
        par_values   = c(1),
        design_space = c(1, 5),
        rival_model  = y ~ b * x,
        rival_params = c("b"),
        rival_pars   = c(3),
        family       = "Normal",
        phi          = 1)

Plot function for optdes

Description

For single-factor models, overlays the support points on the sensitivity function curve. For two-factor models, shows a heatmap of the sensitivity function with the support points overlaid and the Equivalence Theorem contour highlighted. For models with more than two factors, shows a pairwise scatter matrix with one panel per pair of design variables and point size proportional to weight.

Usage

## S3 method for class 'optdes'
plot(x, ...)

Arguments

x

An object of class optdes.

...

Possible extra arguments (currently unused).

Examples

rri <- opt_des(criterion = "I-Optimality", model = y ~ a * exp(-b / x),
  parameters = c("a", "b"), par_values = c(1, 1500), design_space = c(212, 422),
  reg_int = c(380, 422))
plot(rri)

Plot Convergence of the algorithm

Description

Plots the criterion value on each of the steps of the algorithm, both for optimizing weights and points, against the total step number.

Usage

plot_convergence(convergence)

Arguments

convergence

A dataframe with two columns:

criteria contains value of the criterion on each step.
step contains number of the step.

Value

A ggplot object with the criteria in the y axis and step in the x axis.

Plot sensitivity function

Description

Plots the sensitivity function and the value of the Equivalence Theorem as an horizontal line, which helps assess the optimality of the design of the given sensitivity function.

Usage

plot_sens(min, max, sens_function, criterion_value)

Arguments

min

Minimum of the space of the design, used in the limits of the representation.

max

Maximum of the space of the design, used in the limits of the representation.

sens_function

A single variable function, the sensitivity function.

criterion_value

A numeric value representing the other side of the inequality of the Equivalence Theorem.

Value

A ggplot object that represents the sensitivity function

Print method for augment_region objects

Description

Print method for augment_region objects

Usage

## S3 method for class 'augment_region'
print(x, ...)

Arguments

x

An object of class augment_region returned by get_augment_region.

...

Unused.

Print function for optdes

Description

Print function for optdes

Usage

## S3 method for class 'optdes'
print(x, ...)

Arguments

x

An object of class optdes.

...

Possible extra arguments for printing dataframes

Examples

rri <- opt_des(criterion = "I-Optimality", model = y ~ a * exp(-b / x),
  parameters = c("a", "b"), par_values = c(1, 1500), design_space = c(212, 422),
  reg_int = c(380, 422))
print(rri)

Master function to calculate the sensitivity function

Description

Calculates the sensitivity function given the desired Criterion, an information matrix and other necessary values depending on the chosen criterion.

Usage

sens(Criterion, grad, M, par_int = c(1), matB = NA)

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'

grad

A function in a single variable that returns the partial derivatives vector of the model.

M

Information Matrix for the sensitivity function.

par_int

Numeric vector of the indexes of the parameters of interest for Ds-Optimality.

matB

Matrix resulting from the integration of the one-point Information Matrix along the interest region or lineal matrix for L-Optimality.

Value

The sensitivity function as a matrix of single variable.

Shiny D-augment

Description

Launches the demo Shiny application to D-augment several pre-specified models. Requires an interactive R session; if called non-interactively a message with the web URL is emitted instead.

Usage

shiny_augment()

Examples

shiny_augment()

Shiny Optimal

Description

Launches the demo Shiny application to calculate optimal designs for Antoine's Equation. Requires an interactive R session; if called non-interactively a message with the web URL is emitted instead.

Usage

shiny_optimal()

Examples

shiny_optimal()

Summary function for optdes

Description

Summary function for optdes

Usage

## S3 method for class 'optdes'
summary(object, ...)

Arguments

object

An object of class optdes.

...

Possible extra arguments for the summary

Examples

rri <- opt_des(criterion = "I-Optimality", model = y ~ a * exp(-b / x),
  parameters = c("a", "b"), par_values = c(1, 1500), design_space = c(212, 422),
  reg_int = c(380, 422))
summary(rri)

Trace

Description

Return the mathematical trace of a matrix, the sum of its diagonal elements.

Usage

tr(M)

Arguments

M

The matrix from which to calculate the trace.

Value

The trace of the matrix.

Update Design with new point

Description

Updates a design adding a new point to it. If the added point is closer than delta to an existing point of the design, the two points are merged together as their arithmetic average. Then updates the weights to be equal to all points of the design.

Usage

update_design(design, xmax, delta, new_weight)

Arguments

design

Design to update. It's a dataframe with two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

xmax

The point to add as a numeric value.

delta

Threshold which defines how close the new point has to be to any of the existing ones in order to merge with them.

new_weight

Number with the weight for the new point.

Value

The updated design.

Merge close points of a design

Description

Takes a design and merge together all points that are closer between them than a certain threshold delta.

Usage

update_design_total(design, delta)

Arguments

design

The design to update. It's a dataframe with two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

delta

Threshold which defines how close two points have to be to any of the existing ones in order to merge with them.

Value

The updated design.

Deletes duplicates points

Description

Within a vector of points, deletes points that are close enough (less than the tol parameter). Returns the points without the "duplicates"

Usage

update_sequence(points, tol)

Arguments

points

Points to be updated

tol

Tolerance for which two points are considered the same

Value

The points without duplicates

Update weight D-Optimality

Description

Implementation of the weight update formula for D-Optimality used to optimize the weights of a design, which is to be applied iteratively until no sizable changes happen.

Usage

update_weights(design, sens, k, delta)

Arguments

design

Design to optimize the weights from. It's a dataframe with two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

sens

Sensibility function for the design and model.

k

Number of parameters of the model.

delta

A parameter of the algorithm that can be tuned. Must be 0< delta < 1.

Value

returns the new weights of the design after one iteration.

Update weight Ds-Optimality

Description

Implementation of the weight update formula for Ds-Optimality used to optimize the weights of a design, which is to be applied iteratively until no sizable changes happen.

Usage

update_weightsDS(design, sens, s, delta)

Arguments

design

Design to optimize the weights from. It's a dataframe with two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

sens

Sensibility function for the design and model.

s

number of parameters of interest of the model

delta

A parameter of the algorithm that can be tuned. Must be 0< delta < 1.

Value

returns the new weights of the design after one iteration.

Update weight I-Optimality

Description

Implementation of the weight update formula for I-Optimality used to optimize the weights of a design, which is to be applied iteratively until no sizable changes happen. A-Optimality if instead of the integral matrix the identity function is used.

Usage

update_weightsI(design, sens, crit, delta)

Arguments

design

Design to optimize the weights from. It's a dataframe with two columns:

Point contains the support points of the design.
Weight contains the corresponding weights of the Points.

sens

Sensibility function for the design and model.

crit

Value of the criterion function for I-Optimality.

delta

A parameter of the algorithm that can be tuned. Must be 0< delta < 1.

Value

returns the new weights of the design after one iteration.

Weight function per distribution

Description

Weight function per distribution

Usage

weight_function(model, char_vars, values, distribution = "Normal")

Arguments

model

formula describing the model to use. Must use x as the design variable.

char_vars

character vector with the parameters of the models, as written in the formula

values

numeric vector with the parameters nominal values, in the same order as given in parameters.

distribution

character variable specifying the probability distribution of the response. Can be one of the following:

'Normal', for normal homoscedastic (default)
'Gamma', which can be used for exponential or normal heteroscedastic with constant relative error
'Poisson'
'Logistic'

Value

one variable function that represents the square of the structure of variance, in case of heteroscedastic variance of the response.

Package {optedr}

Cocktail Algorithm implementation for Compound Optimality

Description

Usage

Arguments

Value

See Also

Cocktail Algorithm implementation for D-Optimality

Description

Usage

Arguments

Value

See Also

Cocktail Algorithm implementation for Ds-Optimality

Description

Usage

Arguments

Value

See Also

Cocktail Algorithm implementation for L-, I- and A-Optimality

Description

Usage

Arguments

Value

See Also

Cocktail Algorithm for KL-Optimality

Description

Usage

Arguments

Value

See Also

Master function for the cocktail algorithm, that calls the appropriate one given the criterion.

Description

Usage

Arguments

Value

See Also

Add two designs

Description

Usage

Arguments

Value

Update design given crosspoints and alpha

Description

Usage

Arguments

Value

Augment Design

Description

Usage

Arguments

Value

Examples

Check Inputs

Description

Usage

Arguments

Combinatorial round

Description

Usage

Arguments

Value

Examples

Master function for the criterion function

Description

Usage

Arguments

Value

Calculate crosspoints

Description

Usage

Arguments

Value

D-Augment Design

Description

Usage

Arguments

Value

See Also

Examples