mixtureFineOptim {MixOptim}

Performs a restrict interval optimization

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"))