This is a function to calculate the desirability for each response as well as the overall desirability.
data.frame can be used to plot the overall as well as the desirabilities for each response.
This function serves for a visualization of the desirability approach for multiple response optimization.
needs to be an object of class
numeric value - points per factor to be evaluated –> specifies also the grid size.
list - constraints for the factors in coded values such as list(A > 0.5, B < 0.2).
overall returns a
data.frame with a column for each factor, desirability for each response and a column for the overall desirability.
Thomas Roth firstname.lastname@example.org
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#arbitrary example with random data!!! rsdo = rsmDesign(k = 2, blocks = 2, alpha = "both") set.seed(123) response(rsdo) = data.frame(y = rnorm(nrow(rsdo)), y2 = rnorm(nrow(rsdo))) fits(rsdo) = lm(y ~ A*B + I(A^2) + I(B^2), data = rsdo) fits(rsdo) = lm(y2 ~ A*B + I(A^2) + I(B^2), data = rsdo) desires(rsdo) = desirability(y, -1, 2, scale = c(1, 1), target = "max") desires(rsdo) = desirability(y2, -1, 0, scale = c(1, 1), target = "min") dVals = overall(rsdo, steps = 10, constraints = list(A = c(-0.5,1), B = c(0, 1))) ##Uncomment for visualization of desirabilities #require(lattice) #contourplot(y ~ A*B, data = dVals) #desirability of y #contourplot(y2 ~ A*B, data = dVals) #desirability of y2 #wireframe(overall ~ A*B, shade = TRUE, data = dVals)
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