ofwTune {ofw} | R Documentation |
ofwTune
helps to tune the main parameters: mtry
, nforest
and ntree
for ofwCART or mtry
and nsvm
for ofwSVM.
## Default S3 method: ofwTune(x, y, type="CART", ntree= if(type=="CART") 50 else NULL, nforest= if(type=="CART") 100 else NULL, nsvm= if(type=="SVM") 500 else NULL, mtry.test=seq(5,15,length=3), do.trace=FALSE, nstable=10, weight=FALSE, ...)
x |
A data frame with continuous values. |
y |
A response vector given as a factor (classification only). |
type |
The version of OFW to perform. |
ntree |
Number of trees to grow for each iteration (trees aggregation) in case ofwCART is chosen. |
nforest |
Total number of iterations to run if ofwCART. |
nsvm |
Total number of iterations to run if ofwSVM. |
mtry.test |
Vector defining the number of variables sampled according to the weight vector P to be tested. By default the vector is defined as 'seq(5,15,length=3)'. |
do.trace |
If set to some integer, then current iteration is printed for every do.trace iterations and the number of the first stable variables is output. |
nstable |
Need do.trace set to some integer. Stopping criterion before nforest or nsvm iterations are reached: if the nstable first weighted variables are the same after do.stable iterations, then stop. |
weight |
Should the weighting procedure be applied ? |
... |
not used currently. |
ofwTune
consists in testing either ofwCART or ofwSVM with several variable subsets sizes in the given sequence mtry.test
. For each mtry
, the algorithm is performed twice and the function ofwTune
outputs the intersection length of the first nstable
variables selected with these 2 ofw
. The value mtry
to choose should be the one that gives the largest intersection.
The total number of iteration to tune should then be 2 to 3 times the maximum iterations reached for the optimal mtry
.
In case of ofwCART, to choose ntree
, the user should run ofwTune
with several values of ntree
. Usually, the more the trees the stabler the results.
A list with the following components:
type |
The classifier applied to ofw (either CART or SVM). |
nstable |
The number of stable variables chosen. |
mtry.test |
The different subset size tested. |
param |
A 2 by length(mtry.test) matrix indicating the number of stable variables obtained for each value of mtry . |
iter.max |
A 2 by length(mtry.test) matrix indicating the maximum number of iterations reached for each value of mtry . |
weight |
If TRUE the weighted procedure was performed during the learning. |
The computation of ofwTune
might be slow as it consists in launching ofw $2*$length(mtry.Test
).
Kim-Anh L^e Cao Kim-Anh.Le-Cao@toulouse.inra.fr
Patrick Chabrier Patrick.Chabrier@toulouse.inra.fr
L^e Cao, K-A., Gonc calves, O., Besse, P. and Gadat, S. (2007), Selection of biologically relevant genes with a wrapper stochastic algorithm Statistical Applications in Genetics and Molecular Biology: Vol. 6: Iss.1, Article 29.
## On data set "srbct" #data(srbct) #attach(srbct) #tune.cart <- ofwTune(srbct, as.factor(class), type="CART", ntree=50, nforest=200, mtry.test=seq(5,10,length=2)) #tune.cart #tune.svm <- ofwTune(srbct, as.factor(class), type="SVM", nsvm=500, mtry.test=seq(5,10,length=2)) #tune.svm ##Using do.trace options #tune.cart <- ofwTune(srbct, as.factor(class), type="CART", ntree=50, nforest=200, mtry.test=seq(5,10,length=2), do.trace=50, nstable=5) #tune.cart #tune.svm <- ofwTune(srbct, as.factor(class), type="SVM", nsvm=500, mtry.test=seq(5,10,length=2), do.trace=100, nstable=5) #tune.svm #detach(srbct)