ELABORA UN CODIGO PARA GENERAR LA SUPERPOSICION DE IMAGENES SEGUN LO SIGUIENTE: ##Model 1------------------------------------------- ##function ------------------------------------------ rsmOpt <- function(x, Humedad, Proteinas, Cenizas) { Humedad <- dMin(2.56,7.48) Proteinas <- dMax(48.71,64.84) Cenizas <- dMin(6.09,8.10) HumedadPred <- 3.6433+4.7174*x[1]-5.6071*x[2]-3.8400*x[1]*x[2]+3.1696*x[1]^2+3.6496*x[2]^2 ProteinasPred <- 64.0900-3.1408*x[1]+3.7296*x[2]+2.5525*x[1]*x[2]-2.0919*x[1]^2-2.4119*x[2]^2 CenizasPred <- 8.0433-0.3915*x[1]+0.4665*x[2]+0.3175*x[1]*x[2]-0.2779*x[1]^2-0.3204*x[2]^2 outHumedad <- predict(Humedad, data.frame(HumedadPred=HumedadPred)) outProteinas <- predict(Proteinas, data.frame(ProteinasPred=ProteinasPred)) outCenizas <- predict(Cenizas, data.frame(CenizasPred=CenizasPred)) outCCD.D <- (outHumedad*outProteinas*outCenizas)^(1/3) if(any(abs(x)>1)) { outCCD.D <- 0 } return(outCCD.D) } ###PIPELINE gaControl("real-valued") gaControl("real-valued"=list(selection = "gareal_sigmaSelection")) gaControl("real-valued"=list(crossover = "gareal_blxCrossover")) gaControl("real-valued"=list(mutation = "gareal_rsMutation")) format(Sys.time(), "%H:%M:%OS3") CCDGA <- ga(type = c("real-valued"), fitness = rsmOpt, lower = c(-1, -1), upper = c(1, 1), popSize = 100, pcrossover = 0.6999, pmutation = 0.6999, elitism = 2, maxiter = max_it) format(Sys.time(), "%H:%M:%OS3") summary(CCDGA) plot(CCDGA)

You can use the following code to generate the plot:
library(ggplot2)
library(reshape2)

# Create data frame
df <- data.frame(x = 1:10, y = 1:10, z = 1:10)

# Create a new column with the difference between x and y
df$diff <- df$x - df$y

# Create a