Description Usage Arguments Details Value Author(s) See Also Examples
Model fitting after applying univariate filters
1 2 3 4 5 6 7 8 9 10 |
x |
a data frame containing training data where samples are in rows and features are in columns. |
y |
a numeric or factor vector containing the outcome for each sample. |
form |
A formula of the form |
data |
Data frame from which variables specified in |
subset |
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) |
na.action |
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 be named.) |
contrasts |
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. |
sbfControl |
a list of values that define how this function acts. See |
object |
an object of class |
newdata |
a matrix or data frame of predictors. The object must have non-null column names |
... |
for |
More details on this function can be found at http://caret.r-forge.r-project.org/featureselection.html.
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 foreach package. To run the resamples in parallel, the code for sbf
does not change; prior to the call to sbf
, a parallel backend is registered with foreach (see the examples below).
The modeling and filtering techniques are specified in sbfControl
. Example functions are given in lmSBF
.
for sbf
, an object of class sbf
with elements:
pred |
if |
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 |
metrics |
a character vector of names of the performance measures |
dots |
a list of optional arguments that were passed in |
For predict.sbf
, a vector of predictions.
Max Kuhn
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ## Not run:
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)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.