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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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Install

install.packages('ICAOD')

Monthly Downloads

277

Version

0.9.2

License

GPL (>= 2)

Issues

1

Pull Requests

0

Stars

1

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0

Maintainer

Ehsan Masoudi

Last Published

January 9th, 2017

Functions in ICAOD (0.9.2)

FIM_logisitic_1par

Fisher information matrix for the one-parameter logistic model (1PL or Rasch model).
FIM_exp_3par

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

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

Checking the optimality of a given design with respect to the multi-objective criterion for the 4-parameter logistic model.
FIM_emax_3par

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

Checking optimality of a design with respect to locally D-optimal, minimax D-optimal and standardized maximin D-optimal criteria
FIM_logistic_4par

Fisher information matrix for the four parameter logistic model.
FIM_logistic

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

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

Checking the optimality of a design with respect to the optim-on-the-average criterion
iterate.ICA

Update an object of class 'ICA'
ICAOD

ICAOD: finds optimal designs for nonlinear models
iterate

update the object by running the ICA algorithm for more number of iterations.
mica

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

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

Fisher information matrix for the power logistic model.
FIM_mixed_inhibition

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

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

Fisher information matrix for the log-linear model.
FIM_michaelis

Fisher information matrix for the Michaelis-Menten model.
multica_4pl

Imperialist Competitive Algorithm to find multiple-objective optimal designs for the 4-parameter logistic model
plot.ICA

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

print equivalence object
print.ICA

Print method for an object of class "ICA"
FIM_exp_2par

Fisher information matrix for the two-parameter exponential model.