Plot desirability profile

Description

This function creates a graphical representation of the desirability profiles within the data range. It requires a data frame object generated from an optimization function. You can use a simpler data frame to plot the lines and present a more accurate data from another optimization call.

Usage

desirabilityPlot(functions, plotData, bestValues, desirab, types)

Arguments

Value

A ggplot composite object from patchwork

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

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"))

Performs a full interval optimization

Description

This function performs a full interval optimization (0-1 for each x variable). It allows the creation of the data frame used for plotting.

Usage

mixtureOptim(
  functions,
  desirabilityModel,
  xCount,
  step = 0.01,
  plot = TRUE,
  verbose = TRUE
)

Arguments

Value

A list containg the data regarding the maximum desirability found

Examples

library(MixOptim)
saPred<-function(x) 17.3359 * x[1] + 30.7765 * x[2] + 20.0501 * x[3] + 0.7506 * x[1] * x[2] +
  (-6.3443 * x[1] * x[3]) + (-5.9291 * x[2] * x[3]) +(-25.3093 * x[1] * x[2] * x[3])
pvPred<-function(x)  0.884640 * x[1] + 0.789863 * x[2] + 0.825016 * x[3] +
  (-0.108964 * x[1] * x[2]) + 0.107167 * x[1] * x[3] + (-0.005220 * x[2] * x[3]) +
  1.625246 * x[1] * x[2] * x[3]

funcoes <- c(saPred, pvPred)
saD<-dMin(16.8293856, 31.41170555) #minimun value
pvD<-dMax(0.7796004, 0.9019796) #maximum value
overallD<-dOverall(saD, pvD)

a <- mixtureOptim(funcoes, overallD, 3, step = 0.05, plot = TRUE)
a$bestComposition
# next line will generate a ggplot/patchwork grid
#desirabilityPlot(funcoes, a$plotData, a$bestComposition, list(saD, pvD), c("min", "max"))

Performs a specific range optimization

Description

This function performs an optimization testing within an interval defined by the user using alpha values for each middle point provided. It allows the generation of the data frame required for plotting.

Usage

mixtureRangeOptim(
  functions,
  desirabilityModel,
  midPoints,
  alpha,
  step = 0.01,
  plot = TRUE,
  verbose = TRUE
)

Arguments

Value

A list containg the data regarding the maximum desirability found

Examples

library(MixOptim)
dados <- read.table(header = TRUE, sep = "\t", text = "
ID	TiO2	Vehicle	Extender A	Extender B	Hiding	Scrub
1	0.05	0.20	0.30	0.45	7.8953	533.67
2	0.45	0.20	0.30	0.05	32.862	749
3	0.05	0.60	0.30	0.05	3.721	39.5
4	0.05	0.20	0.70	0.05	9.2751	203.25
5	0.25	0.20	0.30	0.25	20.132	555.25
6	0.05	0.40	0.30	0.25	4.7137	51.75
7	0.05	0.20	0.50	0.25	8.3829	342.75
8	0.25	0.40	0.30	0.05	16.245	84.75
9	0.25	0.20	0.50	0.05	22.639	360.75
10	0.05	0.40	0.50	0.05	5.4645	48
11	0.05	0.33	0.43	0.18	5.8882	76
12	0.18	0.20	0.43	0.18	17.256	386.25
13	0.18	0.33	0.30	0.18	12.351	136
14	0.18	0.33	0.43	0.05	14.499	75.5
15	0.10	0.25	0.35	0.30	10.548	325.75
16	0.30	0.25	0.35	0.10	22.096	359
17	0.10	0.45	0.35	0.10	6.2888	40.75
18	0.10	0.25	0.55	0.10	10.629	136.67
19	0.15	0.30	0.40	0.15	11.777	114")

hiding<-function(x) 67.748*x[1] + 7.291*x[2] + 11.419*x[3] + 14.578*x[4] -
   64.32*x[1]*x[2] + 35.878*x[1]*x[3] - 15.696*x[1]*x[4] - 31.006*x[2]*x[3] -
   38.668*x[2]*x[4] - 6.59*x[3]*x[4]
scrub<-function(x) 3937.5*x[1] + 899.3*x[2] + 502*x[3] + 2354.8*x[4] -
   8227.2*x[1]*x[2] - 3227.4*x[1]*x[3] - 2447.7*x[1]*x[4] - 2435.3*x[2]*x[3] -
   6325.1*x[2]*x[4] - 1050.3*x[3]*x[4]

funcoes2 <- c(hiding, scrub)
des1<-dMax(min(dados$Hiding), max(dados$Hiding))
des2<-dMin(min(dados$Scrub), max(dados$Scrub))
finalD<-dOverall(des1, des2)

# code commented due to process time requirement
#teste <- mixtureRangeOptim(funcoes2, finalD, midPoints = c(0.25, 0.4, 0.5, 0.25),
#    alpha = c(0.2, 0.2, 0.2, 0.2), step = 0.01, plot = TRUE)
#desirabilityPlot(funcoes2, teste$plotData, teste$bestComposition, list(des1, des2),
#    c("max", "min"))
#teste2 <- mixtureRangeOptim(funcoes2, finalD, midPoints = teste$bestComposition,
#    alpha = c(0.01, 0.01, 0.01, 0), step = 0.001, plot = FALSE)
#teste2
#desirabilityPlot(funcoes2, teste$plotData, teste2$bestComposition, list(des1, des2),
#    c("max", "min"))