# overall: Overall Desirability In qualityTools: Statistical Methods for Quality Science

## Description

This is a function to calculate the desirability for each response as well as the overall desirability.
The resulting `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.

## Usage

 `1` ```overall(fdo, steps = 20, constraints, ...) ```

## Arguments

 `fdo` needs to be an object of class `facDesign` containing `fits` and `desires` `steps` numeric value - points per factor to be evaluated –> specifies also the grid size. `constraints` list - constraints for the factors in coded values such as list(A > 0.5, B < 0.2). `...` further arguments.

## Value

`overall` returns a `data.frame` with a column for each factor, desirability for each response and a column for the overall desirability.

## Author(s)

Thomas Roth [email protected]

## References

see `desirability`.

`facDesign`
`rsmDesign`
`desirability`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```#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) ```