Fitting metamodels
modelFit is used to fit a metamodel of class lm, gam,
mars, polymars or km.
modelFit (X,Y, type, ...)
X |
a data.frame containing the design of experiments |
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Y |
a vector containing the response variable |
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type |
represents the method used to fit the model:
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... |
corresponds to the parameter(s) of the model. The list of the needed arguments for each type of models is given below:
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A list with the following components:
X |
a data frame representing the design of experiments |
Y |
a vector representing the response |
type |
the type of metamodel |
model |
a fitted model of the specified class |
and the value of the parameter(s) depending on the fitted model.
D. Dupuy
# A 2D example
Branin <- function(x1,x2) {
x1 <- x1*15-5
x2 <- x2*15
(x2 - 5/(4*pi^2)*(x1^2) + 5/pi*x1 - 6)^2 + 10*(1 - 1/(8*pi))*cos(x1) + 10
}
# a 2D uniform design and the value of the response at these points
X <- matrix(runif(24),ncol=2,nrow=12)
Z <- Branin(X[,1],X[,2])
Y <- (Z-mean(Z))/sd(Z)
# construction of a linear model
modLm <- modelFit(X,Y,type = "Linear",formula=Y~X1+X2+X1:X2+I(X1^2)+I(X2^2))
summary(modLm$model)
## Not run:
# construction of a stepwise-selected model
modStep <- modelFit(X,Y,type = "StepLinear",penalty=log(dim(X)[1]),
formula=Y~X1+X2+X1:X2+I(X1^2)+I(X2^2))
summary(modStep$model)
# construction of an additive model
library(gam)
modAm <- modelFit(X,Y,type = "Additive",formula=Y~s(X1)+s(X2))
summary(modAm$model)
# construction of a MARS model of degree 2
library(mda)
modMARS <- modelFit(X,Y,type = "MARS",degree=2)
print(modMARS$model)
# construction of a PolyMARS model with a penalty parameter equal to 1
library(polspline)
modPolyMARS <- modelFit(X,Y,type = "PolyMARS",gcv=1)
summary(modPolyMARS$model)
# construction of a Kriging model
modKm <- modelFit(X,Y,type = "Kriging")
str(modKm$model)
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