PomaRandForest {POMA} | R Documentation |
PomaRandForest() allows users to perform a classification Random Forest with a MS data matrix using the classical randomForest
R package.
PomaRandForest( data, ntest = 20, ntree = 500, mtry = floor(sqrt(ncol(t(MSnbase::exprs(data))))), nodesize = 1, nvar = 20 )
data |
A MSnSet object. First |
ntest |
Numeric indicating the percentage of observations that will be used as test set. Default is 20% of observations. |
ntree |
Number of trees to grow. |
mtry |
Number of variables randomly sampled as candidates at each split. This value is set sqrt(p) (where p is number of variables in data) by default. |
nodesize |
Minimum size of terminal nodes. By default is equal to 1. |
nvar |
Number of variables to show in the Gini plot. |
A list with all results for Random Forest including plots and data frames.
Pol Castellano-Escuder
A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18–22.
data("st000336") st000336 %>% PomaImpute() %>% PomaRandForest()