Comparison of different types of metamodels
modelComparison fits different metamodels and returns R2 and RMSE criteria relating to each.
modelComparison(X,Y, type="all",K=10,test=NULL,...)
X |
a data.frame containing the design of experiments |
Y |
a vector containing the response variable |
type |
a vector containing the type of models to compare. The default value is |
K |
the number of folds for cross-validation (default value is set at 10) |
test |
a data.frame containing the design and the response of a test set when available, the prediction criteria will be evaluated on the test design (default corresponds to no test set) |
... |
according to the |
A list containing two fields if the argument test equal NULL and three fields otherwise :
Learning |
|
CV |
|
Test |
|
A graphical tool to compare the value of the criteria is proposed.
D. Dupuy
## Not run:
data(dataIRSN5D)
X <- dataIRSN5D[,1:5]
Y <- dataIRSN5D[,6]
data(testIRSN5D)
library(gam)
library(mda)
library(polspline)
crit <- modelComparison(X,Y, type="all",test=testIRSN5D)
crit2 <- modelComparison(X,Y, type=rep("StepLinear",5),test=testIRSN5D,
penalty=c(1,2,5,10,20),formula=Y~.^2)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.