opt_des: Calculates the optimal design for a specified Criterion

In optedr: Calculating Optimal and D-Augmented Designs

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. The parameters allows for the user to customize the parameters for the cocktail algorithm in case the default set does not provide a satisfactory output. Depending on the criterion, additional details are necessary. For 'Ds-Optimality' the par_int parameter is necessary. For 'I-Optimality' either the matB or reg_int must be provided.

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,
  desired_output = c(1, 2),
  distribution = NA,
  weight_fun = function(x) 1
)

Arguments

Criterion	character variable with the chosen optimality criterion. Can be one of the following: 'D-Optimality' 'Ds-Optimality' 'A-Optimality' 'I-Optimality'
model	formula describing the model to calculate the optimal design. Must use x as the variable.
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.
design_space	numeric vector with the limits of the space of the design.
init_design	optional dataframe with the initial design for the algorithm. A dataframe with two columns: Point contains the support points of the design. Weight contains the corresponding weights of the Points.
join_thresh	optional numeric value that states how close, in real units, two points must be in order to be joined together by the join heuristic.
delete_thresh	optional numeric value with the minimum weight, over 1 total, that a point needs to have in order to not be deleted from the design.
delta	optional numeric value in (0, 1), parameter of the algorithm.
tol	optional numeric value for the convergence of the weight optimizing algorithm.
tol2	optional numeric value for the stop criterion: difference between maximum of sensitivity function and optimality criterion.
par_int	optional numeric vector with the index of the parameters of interest for Ds-optimality.
matB	optional matrix of dimensions k x k, integral of the information matrix of the model over the interest region for I-optimality.
reg_int	optional numeric vector of two components with the bounds of the interest region for I-Optimality.
desired_output	not functional yet: decide which kind of output you want.
distribution	character variable 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

Value

a list of two objects:

• optdes: a dataframe with the optimal design in two columns, Point and Weight.

• sens: a plot with the sensitivity function to check for optimality of the design.

Examples

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

optedr documentation built on Nov. 18, 2022, 5:12 p.m.