RWeka (version 0.4-43)

Weka_classifier_functions: R/Weka Classifier Functions


R interfaces to Weka regression and classification function learners.


LinearRegression(formula, data, subset, na.action,
                 control = Weka_control(), options = NULL)
Logistic(formula, data, subset, na.action,
         control = Weka_control(), options = NULL)
SMO(formula, data, subset, na.action,
    control = Weka_control(), options = NULL)



a symbolic description of the model to be fit.


an optional data frame containing the variables in the model.


an optional vector specifying a subset of observations to be used in the fitting process.


a function which indicates what should happen when the data contain NAs. See model.frame for details.


an object of class Weka_control giving options to be passed to the Weka learner. Available options can be obtained on-line using the Weka Option Wizard WOW, or the Weka documentation.


a named list of further options, or NULL (default). See Details.


A list inheriting from classes Weka_functions and Weka_classifiers with components including


a reference (of class jobjRef) to a Java object obtained by applying the Weka buildClassifier method to build the specified model using the given control options.


a numeric vector or factor with the model predictions for the training instances (the results of calling the Weka classifyInstance method for the built classifier and each instance).


the matched call.


There are a predict method for predicting from the fitted models, and a summary method based on evaluate_Weka_classifier.

LinearRegression builds suitable linear regression models, using the Akaike criterion for model selection.

Logistic builds multinomial logistic regression models based on ridge estimation (le Cessie and van Houwelingen, 1992).

SMO implements John C. Platt's sequential minimal optimization algorithm for training a support vector classifier using polynomial or RBF kernels. Multi-class problems are solved using pairwise classification.

The model formulae should only use the + and - operators to indicate the variables to be included or not used, respectively.

Argument options allows further customization. Currently, options model and instances (or partial matches for these) are used: if set to TRUE, the model frame or the corresponding Weka instances, respectively, are included in the fitted model object, possibly speeding up subsequent computations on the object. By default, neither is included.


J. C. Platt (1998). Fast training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf, C. Burges, and A. Smola (eds.), Advances in Kernel Methods --- Support Vector Learning. MIT Press.

I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.

See Also



## Linear regression:
## Using standard data set 'mtcars'.
LinearRegression(mpg ~ ., data = mtcars)
## Compare to R:
step(lm(mpg ~ ., data = mtcars), trace = 0)

## Using standard data set 'chickwts'.
LinearRegression(weight ~ feed, data = chickwts)
## (Note the interactions!)

## Logistic regression:
## Using standard data set 'infert'.
STATUS <- factor(infert$case, labels = c("control", "case"))
Logistic(STATUS ~ spontaneous + induced, data = infert)
## Compare to R:
glm(STATUS ~ spontaneous + induced, data = infert, family = binomial())

## Sequential minimal optimization algorithm for training a support
## vector classifier, using am RBF kernel with a non-default gamma
## parameter (argument '-G') instead of the default polynomial kernel
## (from a question on r-help):
SMO(Species ~ ., data = iris,
    control = Weka_control(K =
    list("weka.classifiers.functions.supportVector.RBFKernel", G = 2)))
## In fact, by some hidden magic it also "works" to give the "base" name
## of the Weka kernel class:
SMO(Species ~ ., data = iris,
    control = Weka_control(K = list("RBFKernel", G = 2)))
# }