Evolution of the stepwise model
Graphical representation of the selected terms using stepwise procedure for different values of the penalty parameter.
stepEvolution(X,Y,formula,P=1:7,K=10,test=NULL,graphic=TRUE)
X |
a data.frame containing the design of experiments |
Y |
a vector containing the response variable |
formula |
a formula for the initial model |
P |
a vector containing different values of the penalty parameter for which a stepwise selected model is fitted |
K |
the number of folds for the cross-validation procedure |
test |
an additional data set on which the prediction criteria are evaluated (default corresponds to no test data set) |
graphic |
if |
a list with the different criteria for different values of the penalty parameter. This list contains:
penalty |
the values for the penalty parameter |
m |
size |
R2 |
the value of the |
According to the value of the test argument, other criteria are calculated:
| a. | If a test set is available, R2test contains the value of the R2
criterion on the test set |
| b. | If no test set is available, the Q2 and the RMSE computed by
cross-validation are done.
|
Plots are also available.
A tabular represents the selected terms for each value in P.
The evolution of the R2 criterion, the evolution of the size m of the selected
model and criteria on the test set or by K-folds cross-validation are represented.
These graphical tools can be used to select the best value for the penalty parameter.
D. Dupuy
step procedure for linear models.
## Not run: data(dataIRSN5D) design <- dataIRSN5D[,1:5] Y <- dataIRSN5D[,6] out <- stepEvolution(design,Y,formulaLm(design,Y),P=c(1,2,5,10,20,30)) ## End(Not run)
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