check_inputs: Check Inputs

In optedr: Calculating Optimal and D-Augmented Designs

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,
  desired_output,
  weight_fun
)

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

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