predict.dMax {desirability}

Predict method for desirability functions

Description

Predicted values based on desirability objects

Usage

## S3 method for class 'dMax'
predict(object, newdata = NA, missing = object$missing, ...)
## S3 method for class 'dMin'
predict(object, newdata = NA, missing = object$missing, ...)
## S3 method for class 'dTarget'
predict(object, newdata = NA, missing = object$missing, ...)
## S3 method for class 'dArb'
predict(object, newdata = NA, missing = object$missing, ...)
## S3 method for class 'dBox'
predict(object, newdata = NA, missing = object$missing, ...)
## S3 method for class 'dCategorical'
predict(object, newdata = NA, missing = object$missing, ...)
## S3 method for class 'dOverall'
predict(object, newdata = data.frame(NA, ncol = length(object$d)), all = FALSE, ...)

Arguments

object

a object of class: dMax, dMin, dTarget, dArb, dBox or dOverall

newdata

values of the response for predicting desirability

all

a logical (for predict.dOverall only); should the individual desirabilities also be returned?

missing

a number between 0 and 1 for missing values (the internally estimated value is used by default)

...

no currently used

Details

The responses are translated into desirability units.

Value

a vector, unless predict.dOverall is used with all=TRUE, in which case a matrix is returned.

Author(s)

Max Kuhn

References

Derringer, G. and Suich, R. (1980), Simultaneous Optimization of Several Response Variables. Journal of Quality Technology 12, 214–219.

See Also

dMax

Examples

d1 <- dMin(1,3)
d2 <- dTarget(1, 2, 3)
d3 <- dCategorical(c("a" = .1, "b" = .7))
dAll <- dOverall(d1, d2, d3)

outcomes <- data.frame(seq(0, 4, length = 10),
                       seq(0.5, 4.5, length = 10),
                       sample(letters[1:2], 10, replace = TRUE))
names(outcomes) <- c("x1", "x1", "x3")   
  
predict(d1, outcomes[,2])
predict(d2, outcomes[,2])
predict(d3, outcomes[,3])
predict(dAll, outcomes)
predict(dAll, outcomes, all = TRUE)