check_inputs: Check Inputs
In Kezrael/optedr: Calculating Optimal and D-Augmented Designs

check_inputs

R Documentation

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' 'L-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 `Point`s.
`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, for L-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.