Description Usage Arguments Details Value Author(s) References See Also Examples
Predicted values based on desirability objects
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## 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, ...)
|
object |
a object of class: |
newdata |
values of the response for predicting desirability |
all |
a logical (for |
missing |
a number between 0 and 1 for missing values (the internally estimated value is used by default) |
... |
no currently used |
The responses are translated into desirability units.
a vector, unless predict.dOverall
is used with all=TRUE
,
in which case a matrix is returned.
Max Kuhn
Derringer, G. and Suich, R. (1980), Simultaneous Optimization of Several Response Variables. Journal of Quality Technology 12, 214–219.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | 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)
|
[1] 1.0000000 1.0000000 0.8055556 0.5833333 0.3611111 0.1388889 0.0000000
[8] 0.0000000 0.0000000 0.0000000
[1] 0.0000000 0.0000000 0.3888889 0.8333333 0.7222222 0.2777778 0.0000000
[8] 0.0000000 0.0000000 0.0000000
[1] 0.1 0.1 0.1 0.1 0.1 0.1 0.7 0.1 0.7 0.1
[1] 0.0000000 0.0000000 0.3387988 0.4110353 0.3533977 0.2210587 0.0000000
[8] 0.0000000 0.0000000 0.0000000
D1 D2 D3 Overall
1 1.0000000 0.0000000 0.1 0.0000000
2 1.0000000 0.0000000 0.1 0.0000000
3 1.0000000 0.3888889 0.1 0.3387988
4 0.8333333 0.8333333 0.1 0.4110353
5 0.6111111 0.7222222 0.1 0.3533977
6 0.3888889 0.2777778 0.1 0.2210587
7 0.1666667 0.0000000 0.7 0.0000000
8 0.0000000 0.0000000 0.1 0.0000000
9 0.0000000 0.0000000 0.7 0.0000000
10 0.0000000 0.0000000 0.1 0.0000000
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