crossval {LogitBoost} | R Documentation |
The data are divided into v non-overlapping subsets of roughly equal size. Then, feature selection is applied on (v-1) of the subsets, which are also used to fit the LogitBoost classifier. Then, predictions are made for the left out subsets, and the process is repeated for each of the v subsets.
crossval(x, y, v=length(y), mfinal=100, presel=0, estimate=0, verbose=F)
x |
A matrix with n rows (different individuals) and p columns (different genes) containing expression values. |
y |
A vector of length n containing the class labels from individuals of K different classes. The labels need to be coded by consecutive integers from 0 to (K-1). |
v |
An integer, specifying the type of v-fold cross validation. The default, v=length(y) means leave-one-out cross validation. Besides this, every value between 2 and length(y) is valid and means that roughly every v-th observation is left out. Make sure that (especially for multiclass problems) this is a sensible partition into training and test data. |
mfinal |
An integer, describing the number of iterations for which boosting should be run. The default value is mfinal=100, which is a reasonable choice for gene expression data. |
presel |
An integer, giving the number of features to be used for classification. If presel=0, no feature preselection is carried out. |
estimate |
An integer, specifying the v of an additional, internal v-fold cross validation on the respective training data for stopping parameter estimation. Please note that this is (especially for larger values of 'estimate') extremly time consuming. The default value of estimate=0 means no stopping parameter estimation. |
verbose |
Logical, indicates whether comments should be given. |
The computation of the stopping parameter estimate is computationally very expensive and time consuming.
probs |
Array, whose rows contain out of sample probabilities that the class labels are predicted as 1, for every boosting iteration. For multiclass problems, the third dimension of the array are the probabilites for the K binary one-against-all partitions of the data. |
loglikeli |
Array, contains the log-likelihood across the training instances for determination of the stopping parameter if estimate>0. For multiclass problems, the third dimension of the array contains the values for the K binary one-against-all partitions of the data. |
Marcel Dettling
See "Boosting for Tumor Classification of Gene Expression Data", Dettling and Buhlmann (2002), available on the web page http://stat.ethz.ch/~dettling/boosting.html
data(leukemia) ## An example without stopping parameter estimation fit <- crossval(leukemia.x,leukemia.y,v=5,mfinal=100,presel=75,verbose=TRUE) summarize(fit,leukemia.y) ## 4-fold cross validation with stopping estimation by 3-fold-cv fit <- crossval(leukemia.x,leukemia.y,v=4,presel=50,estimate=3,verbose=TRUE) summarize(fit,leukemia.y)