ICA.control {ICAOD}

Returns ICA Control Optimization Parameters

Description

The function ICA.control returns a list of ICA control parameters.

Usage

ICA.control(
  ncount = 40,
  nimp = ncount/10,
  assim_coeff = 4,
  revol_rate = 0.3,
  damp = 0.99,
  uniting_threshold = 0.02,
  equal_weight = FALSE,
  sym = FALSE,
  sym_point = NULL,
  stop_rule = c("maxiter", "equivalence"),
  stoptol = 0.99,
  checkfreq = 0,
  plot_cost = TRUE,
  plot_sens = TRUE,
  plot_3d = c("lattice", "rgl"),
  trace = TRUE,
  rseed = NULL
)

Arguments

ncount

Number of countries. Defaults to 40.

nimp

Number of imperialists. Defaults to 10 percent of ncount.

assim_coeff

Assimilation coefficient. Defaults to 4.

revol_rate

Revolution rate. Defaults to 0.3.

damp

Damp ratio for revolution rate. revol_rate is decreased in every iteration by a factor of damp (revol_rate * damp). Defaults to 0.99.

uniting_threshold

If the distance between two imperialists is less than the product of the uniting threshold by the largest distance in the search space, ICA unites the empires. Defaults to 0.02.

equal_weight

Should the weights of design points assumed to be equal? Defaults to FALSE. If TRUE, it reduces the dimension of the search space and produces a design that gives equal weight to all of its support points.

sym

Should the design points be symmetric around sym_point? Defaults to FALSE. When TRUE, sym_point must be given.

sym_point

If sym = TRUE, the design points will be symmetric around sym_point. See 'Details'.

stop_rule

Either 'maxiter' or 'equivalence'. Denotes the type of stopping rule. See 'Details'. Defaults to 'maxiter'.

stoptol

If stop_rule = 'equivalence', algorithm stops when ELB is larger than stoptol. Defaults to 0.99.

checkfreq

The algorithm verifies the general equivalence theorem in every checkfreq iterations. When checkfreq = 0, no verification will be done. When checkfreq = Inf, only the output design will be verified. Defaults to 0.

plot_cost

Plot the iterations (evolution) of algorithm? Defaults to TRUE.

plot_sens

Plot the sensitivity (derivative) function at every checkfreq. Defaults to TRUE.

plot_3d

Character. Which package should be used to plot the sensitivity plot for models with two explanatory variables?

trace

Print the information in every iteration? Defaults to TRUE.

rseed

Random seed. Defaults to NULL.

Details

If stop_rule = 'maxiter', the algorithm iterates until maximum number of iterations.
If stope_rule = 'equivalence', the algorithm stops when either ELB is greater than stoptol or it reaches maxiter. In this case, you must specify the check frequency by checkfreq. Note that checking equivalence theorem is a very time consuming process, especially for Bayesian and minimax design problems. We advise using this option only for locally, multiple objective and robust optimal designs.

What to follows shows how sym_point and sym may be useful?
Assume the 2PL model of the form P(Y=1) = \frac{1}{1+exp(-b(x - a))} and let the parameters a and b belong to [a_L, a_U] and [b_L, b_U], respectively. It can be shown that the optimal design for this model is symmetric around a_M = \frac{a_L + a_U}{2}. For this model, to find accurate symmetric designs, one can set sym = TRUE and provide the value of the a_M via sym_point. In this case, the output design will be symmetric around the point sym_point. The length of sym_point must be equal to the number of model predictors, here, is equal to 1.

Value

A list of ICA control parameters.

Examples

ICA.control(ncount = 100)