mlearning (version 1.2.1)

mlQda: Supervised classification using quadratic discriminant analysis

Description

Unified (formula-based) interface version of the quadratic discriminant analysis algorithm provided by MASS::qda().

Usage

mlQda(train, ...)
ml_qda(train, ...)
# S3 method for formula
mlQda(formula, data, ..., subset, na.action)
# S3 method for default
mlQda(train, response, ...)
# S3 method for mlQda
predict(
  object,
  newdata,
  type = c("class", "membership", "both"),
  prior = object$prior,
  method = c("plug-in", "predictive", "debiased", "looCV", "cv"),
  ...
)

Value

ml_qda()/mlQda() creates an mlQda, mlearning object containing the classifier and a lot of additional metadata used by the functions and methods you can apply to it like predict() or cvpredict(). In case you want to program new functions or extract specific components, inspect the "unclassed" object using unclass().

Arguments

train

a matrix or data frame with predictors.

...

further arguments passed to MASS::qda() or its predict() method (see the corresponding help page).

formula

a formula with left term being the factor variable to predict and the right term with the list of independent, predictive variables, separated with a plus sign. If the data frame provided contains only the dependent and independent variables, one can use the class ~ . short version (that one is strongly encouraged). Variables with minus sign are eliminated. Calculations on variables are possible according to usual formula convention (possibly protected by using I()).

data

a data.frame to use as a training set.

subset

index vector with the cases to define the training set in use (this argument must be named, if provided).

na.action

function to specify the action to be taken if NAs are found. For ml_qda() na.fail is used by default. The calculation is stopped if there is any NA in the data. Another option is na.omit, where cases with missing values on any required variable are dropped (this argument must be named, if provided). For the predict() method, the default, and most suitable option, is na.exclude. In that case, rows with NAs in newdata= are excluded from prediction, but reinjected in the final results so that the number of items is still the same (and in the same order as newdata=).

response

a vector of factor for the classification.

object

an mlQda object

newdata

a new dataset with same conformation as the training set (same variables, except may by the class for classification or dependent variable for regression). Usually a test set, or a new dataset to be predicted.

type

the type of prediction to return. "class" by default, the predicted classes. Other options are "membership" the membership (a number between 0 and 1) to the different classes, or "both" to return classes and memberships.

prior

the prior probabilities of class membership. By default, the prior are obtained from the object and, if they where not changed, correspond to the proportions observed in the training set.

method

"plug-in", "predictive", "debiased", "looCV", or "cv". "plug-in" (default) the usual unbiased parameter estimates are used. With "predictive", the parameters are integrated out using a vague prior. With "debiased", an unbiased estimator of the log posterior probabilities is used. With "looCV", the leave-one-out cross-validation fits to the original data set are computed and returned. With "cv", cross-validation is used instead. If you specify method = "cv" then cvpredict() is used and you cannot provide newdata= in that case.

See Also

mlearning(), cvpredict(), confusion(), also MASS::qda() that actually does the classification.

Examples

# Prepare data: split into training set (2/3) and test set (1/3)
data("iris", package = "datasets")
train <- c(1:34, 51:83, 101:133)
iris_train <- iris[train, ]
iris_test <- iris[-train, ]
# One case with missing data in train set, and another case in test set
iris_train[1, 1] <- NA
iris_test[25, 2] <- NA

iris_qda <- ml_qda(data = iris_train, Species ~ .)
summary(iris_qda)
confusion(iris_qda)
confusion(predict(iris_qda, newdata = iris_test), iris_test$Species)

# Another dataset (binary predictor... not optimal for qda, just for test)
data("HouseVotes84", package = "mlbench")
house_qda <- ml_qda(data = HouseVotes84, Class ~ ., na.action = na.omit)
summary(house_qda)