majority: Majority Classifier
In schiffner/locClass: Collection of Local Classification Methods

Description Usage Arguments Details Value See Also Examples

A classifier that always predicts the class with the highest weighted prior probability.

majority(x, ...)

## S3 method for class 'formula'
majority(formula, data, weights = rep(1, nrow(data)), ...,
  subset, na.action)

## S3 method for class 'data.frame'
majority(x, ...)

## S3 method for class 'matrix'
majority(x, grouping, weights = rep(1, nrow(x)), ..., subset,
  na.action = na.fail)

## Default S3 method:
majority(x, grouping, weights = rep(1, nrow(x)), ...)

`x`	(Required if no `formula` is given as principal argument.) A `matrix` or `data.frame` or `Matrix` containing the explanatory variables.
`formula`	A `formula` of the form `groups ~ x1 + x2 + ...`, that is, the response is the grouping `factor` and the right hand side specifies the discriminators.
`data`	A `data.frame` from which variables specified in `formula` are to be taken.
`weights`	Observation weights to be used in the fitting process, must be non-negative.
`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 `NA`'s are found. The default action is first the `na.action` setting of `options` and second `na.fail` if that is unset. 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.)
`grouping`	(Required if no `formula` is given as principal argument.) A `factor` specifying the class membership for each observation.
`...`	Further arguments.

The majority classifier usually serves to determine a baseline for more sophisticated classification methods. This function is also a helper function needed to combine mixture models and recursive partitioning with a majority classifier. The weighted prior probabilities are calculated as

p_g = ∑_{n:y_n=g} w_n/(∑_n w_n)

An object of class "majority", a list containing the following components:

`prior`	Weighted class prior probabilities.
`counts`	The number of observations per class.
`lev`	The class labels (levels of `grouping`).
`N`	The number of observations.
`weights`	The observation weights used in the fitting process.
`predictors`	The names of the predictor variables.
`call`	The (matched) function call.

Other majority: majorityGenerative, predict.majorityGenerative, predict.majority

library(mlbench)
data(PimaIndiansDiabetes)

train <- sample(nrow(PimaIndiansDiabetes), 500)

# weighting observations from classes pos and neg according to their
# frequency in the data set:
ws <- as.numeric(1/table(PimaIndiansDiabetes$diabetes)
    [PimaIndiansDiabetes$diabetes])

fit <- majority(diabetes ~ ., data = PimaIndiansDiabetes, weights = ws,
    subset = train)
pred <- predict(fit, newdata = PimaIndiansDiabetes[-train,])
mean(pred$class != PimaIndiansDiabetes$diabetes[-train])