# Performs a restrict interval optimization 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. ```r 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) ## Returns A list containg the data regarding the maximum desirability found ## Examples ```r 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")) ```

mixtureFineOptim function