mixtureFineOptim: Performs a restrict interval optimization

In MixOptim: Mixture Optimization Algorithm

Description

This function performs an optimization testing within an interval defined by the user using starting points and an alpha value. Since it is designed for more accurate searching, it does not allow the generation of the data frame for plotting.

Usage

mixtureFineOptim(
  functions,
  desirabilityModel,
  startPoint,
  step = 0.001,
  alpha = 0.02,
  verbose = TRUE
)

Arguments

functions	An array of functions
desirabilityModel	A desirability overallD model
startPoint	An array with the references (mid-points) for the optimization
step	The ammount of each increment in the optimization
alpha	Defines the range of the seach, as startPoint +- alpha for each x value
verbose	Defines if the user should be updated with the processing status (percentages)

Value

A list containg the data regarding the maximum desirability found

Examples

library(MixOptim)
dados <- read.table(header = TRUE, dec = ",", sep = "\t", text = "
x1	x2	x3	R1	R2	R3
1	0	0	0,76	8	5
1	0	0	0,75	8	5
0,5	0,5	0	1,4	7	7,5
0,5	0	0,5	0,55	8	10
0	1	0	4,1	4	10
0	1	0	4,4	4	10
0	0,5	0,5	0,9	7	12,5
0	0	1	0,42	9	15
0	0	1	0,4	10	15
0,6667	0,1667	0,1667	0,8	7	7,5
0,1667	0,6667	0,1667	1,7	7	10
0,1667	0,1667	0,6667	0,55	8	12,5
0,3333	0,3333	0,3333	0,8	8	10")

lm1 <- lm(data = dados, R1 ~ -1 + x1 + x2 + x3 + x1:x2 + x1:x3 + x2:x3)
summary(lm1)
flm1 <- function(x) 0.7678*x[1] + 4.2083*x[2] + 0.4274*x[3] - 4.3273*x[1]*x[2] +
      0.3070*x[1]*x[3] - 5.6101*x[2]*x[3]
lm2 <- lm(data = dados, R2 ~ -1 + x1 + x2 + x3)
summary(lm2)
flm2 <- function(x) 7.9742*x[1] + 4.5742*x[2] + 9.3742*x[3]
lm3 <- lm(data = dados, R3 ~ -1 + x1 + x2 + x3)
summary(lm3)
flm3 <- function(x) 4.9998461*x[1] + 9.9998461*x[2] + 14.9998461*x[3]

funcoes2 <- c(flm1, flm2, flm3)
des1<-dTarget(0.5, 0.6, 0.7)
des2<-dMax(8, max(dados$R2))
des3<-dMin(5, 10)
finalD<-dOverall(des1, des2, des3)

# code commented due to process time requirement
#teste <- mixtureOptim(funcoes2, finalD, 3, step = 0.01, plot = TRUE)
#desirabilityPlot(funcoes2, teste$plotData, teste$bestComposition,
#   list(des1, des2, des3), c("max", "max", "min"))

#teste2 <- mixtureFineOptim(funcoes2, finalD, teste$bestComposition, step = 0.0001)
#desirabilityPlot(funcoes2, teste$plotData, teste2$bestComposition,
#   list(des1, des2, des3), c("max", "max", "min"))

MixOptim documentation built on July 1, 2020, 9:57 p.m.