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Imperialist Competitive Algorithm (ICA) to find optimal designs for nonlinear models.

Example: locally D-optimal design for the exponential model mica(fimfunc = "FIM_exp_2par", lx = 0, ux = 1, lp = c(2, 3), up = c(2, 3), iter = 40, k = 2, type = "locally", control = list(seed = 215))

How to isntall:

install.packages("ICAOD") require(ICAOD)

The most important function is mica that finds locally, minimax and standardized maximin D-optimal design for nonlinear models. on_average_ica also finds optim on the average optimal designs.

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Version

Install

install.packages('ICAOD')

Monthly Downloads

277

Version

0.9.1

License

GPL (>= 2)

Issues

1

Pull Requests

0

Stars

1

Forks

0

Maintainer

Ehsan Masoudi

Last Published

August 10th, 2016

Functions in ICAOD (0.9.1)

FIM_loglin

Fisher information matrix for the log-linear model.
FIM_uncomp_inhibition

Fisher information matrix for the uncompetitive inhibition Michaelis-Menten model.
iterate

update the object of class 'ICA'.
FIM_noncomp_inhibition

Fisher information matrix for the noncompetitive inhibition Michaelis-Menten model.
iterate.ICA

Update an object of class 'ICA'
FIM_mixed_inhibition

Fisher information matrix for the mixed inhibition Michaelis-Menten model.
multica_4pl

Imperialist Competitive Algorithm to find multiple-objective optimal designs for the 4-parameter logistic models.
mica

Imperialist Competitive Algorithm to find locally, minimax and standardized maximin D-optimal designs for nonlinear models
FIM_power_logistic

Fisher information matrix for the power logistic model.
FIM_michaelis

Fisher information matrix for the Michaelis-Menten model.
on_average_ica

Imperialist Competitive Algorithm to find optimum on-the-average designs based on the least favorable distribution.
print.ICA

Print method for an object of class "ICA"
plot.ICA

Plot method for an object of class "ICA".
print.equivalence

print equivalence object
FIM_comp_inhibition

Fisher information matrix for the competitive inhibition Michaelis-Menten model.
FIM_exp_3par

Fisher information matrix for the three-parameter exponential model.
equivalence

Checking minimax, standardized maximin and locally D-optimality of a givne design by equivalence theorem
FIM_emax_3par

Fisher information matrix for the three-parameter emax model.
equivalence_multiple

Checking the optimality of a given design with respect to the multi-objective criterion for the 4-parameter logisitic model.
FIM_logisitic_1par

Fisher information matrix for the one-parameter logistic model (1PL or Rasch model).
FIM_logistic_4par

Fisher information matrix for the four parameter logistic model.
FIM_logistic

Fisher information matrix for the two-parameter logistic (2PL) model.
equivalence_on_average

Checking the optimality of a given design with respect to the optim-on-the-average criterion by equivalence theorem.