# Overall Desirability

### 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 |

### Arguments

`fdo` |
needs to be an object of class |

`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 thomas.roth@tu-berlin.de

### References

see `desirability`

.

### See Also

`facDesign`

`rsmDesign`

`desirability`

http://www.r-qualitytools.org/html/Improve.html

### Examples

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)
``` |