Fitting metamodels

Description

modelFit is used to fit a metamodel of class lm, gam, mars, polymars or km.

Usage

modelFit (X,Y, type, ...)

Arguments

Value

A list with the following components:

and the value of the parameter(s) depending on the fitted model.

Author(s)

D. Dupuy

See Also

modelPredict

Examples

# 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)

## End(Not run)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(DiceEval)
Loading required package: DiceKriging
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DiceEval/modelFit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: modelFit
> ### Title: Fitting metamodels
> ### Aliases: modelFit
> ### Keywords: models
> 
> ### ** Examples
> 
> # 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)

Call:
lm(formula = fmla, data = data)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.237530 -0.093927  0.003293  0.045156  0.261372 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -1.2297     0.7406  -1.660   0.1479  
X1            0.9225     2.0161   0.458   0.6634  
X2           -0.6638     1.4531  -0.457   0.6639  
I(X1^2)      -0.8991     1.4373  -0.626   0.5546  
I(X2^2)       1.4036     1.4143   0.992   0.3593  
X1:X2         2.9662     1.0640   2.788   0.0317 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1986 on 6 degrees of freedom
Multiple R-squared:  0.9785,	Adjusted R-squared:  0.9606 
F-statistic:  54.6 on 5 and 6 DF,  p-value: 6.37e-05

> 
> ## Not run: 
> ##D # construction of a stepwise-selected model 
> ##D modStep <- modelFit(X,Y,type = "StepLinear",penalty=log(dim(X)[1]),
> ##D 		formula=Y~X1+X2+X1:X2+I(X1^2)+I(X2^2))
> ##D summary(modStep$model)
> ##D 
> ##D # construction of an additive model
> ##D library(gam)
> ##D modAm <- modelFit(X,Y,type = "Additive",formula=Y~s(X1)+s(X2))
> ##D summary(modAm$model)
> ##D 
> ##D # construction of a MARS model of degree 2
> ##D library(mda)
> ##D modMARS <- modelFit(X,Y,type = "MARS",degree=2)
> ##D print(modMARS$model)
> ##D 
> ##D # construction of a PolyMARS model with a penalty parameter equal to 1
> ##D library(polspline)
> ##D modPolyMARS <- modelFit(X,Y,type = "PolyMARS",gcv=1)
> ##D summary(modPolyMARS$model)
> ##D 
> ##D # construction of a Kriging model
> ##D modKm <- modelFit(X,Y,type = "Kriging")
> ##D str(modKm$model)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>