Selection By Filtering (SBF)
Model fitting after applying univariate filters
## S3 method for class 'default': sbf(x, y, sbfControl = sbfControl(), ...)
## S3 method for class 'formula': sbf(form, data, ..., subset, na.action, contrasts = NULL)
## S3 method for class 'sbf': predict(object, newdata = NULL, ...)
- a data frame containing training data where samples are in rows and features are in columns.
- a numeric or factor vector containing the outcome for each sample.
- A formula of the form
y ~ x1 + x2 + ...
- Data frame from which variables specified in
formulaare preferentially to be taken.
- An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
- A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must
- a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
- a list of values that define how this function acts. See
sbfControl. (NOTE: If given, this argument must be named.)
- an object of class
- a matrix or data frame of predictors. The object must have non-null column names
sbf: arguments passed to the classification or regression routine (such as
predict.sbf: augments cannot be passed to the prediction function
This function can be used to get resampling estimates for models when simple, filter-based feature selection is applied to the training data.
For each iteration of resampling, the predictor variables are univariately filtered prior to modeling. Performance of this approach is estimated using resampling. The same filter and model are then applied to the entire training set and the final model (and final features) are saved.
sbf can be used with "explicit parallelism", where different resamples (e.g. cross-validation group) can be split up and run on multiple machines or processors. By default,
sbf will use a single processor on the host machine. As of version 4.99 of this package, the framework used for parallel processing uses the
sbf does not change; prior to the call to
sbf, a parallel backend is registered with
sbf, an object of class
TRUE, this is a list of predictions for the hold-out samples at each resampling iteration. Otherwise it is
variables a list of variable names that survived the filter at each resampling iteration results a data frame of results aggregated over the resamples fit the final model fit with only the filtered variables optVariables the names of the variables that survived the filter using the training set call the function call control the control object resample if
sbfControl$returnResampis "all", a data frame of the resampled performance measures. Otherwise,
metrics a character vector of names of the performance measures dots a list of optional arguments that were passed in
predict.sbf, a vector of predictions.
data(BloodBrain) ## Use a GAM is the filter, then fit a random forest model RFwithGAM <- sbf(bbbDescr, logBBB, sbfControl = sbfControl(functions = rfSBF, verbose = FALSE, method = "cv")) RFwithGAM predict(RFwithGAM, bbbDescr[1:10,]) ## classification example with parallel processing ## library(doMC) ## Note: if the underlying model also uses foreach, the ## number of cores specified above will double (along with ## the memory requirements) ## registerDoMC(cores = 2) data(mdrr) mdrrDescr <- mdrrDescr[,-nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .8)] set.seed(1) filteredNB <- sbf(mdrrDescr, mdrrClass, sbfControl = sbfControl(functions = nbSBF, verbose = FALSE, method = "repeatedcv", repeats = 5)) confusionMatrix(filteredNB)