mlearning (version 1.2.1)

mlLda: Supervised classification using linear discriminant analysis

Description

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

Usage

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

Value

ml_lda()/mlLda() creates an mlLda, 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::lda() 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_lda() 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 mlLda 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. The type = "projection" returns a projection of the individuals in the plane represented by the dimension= discriminant components.

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.

dimension

the number of the predictive space to use. If NULL (the default) a reasonable value is used. If this is less than min(p, ng-1), only the first dimension discriminant components are used (except for method = "predictive"), and only those dimensions are returned in x.

method

"plug-in", "predictive", "debiased", 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 "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::lda() 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_lda <- ml_lda(data = iris_train, Species ~ .)
iris_lda
summary(iris_lda)
plot(iris_lda, col = as.numeric(response(iris_lda)) + 1)
# Prediction using a test set
predict(iris_lda, newdata = iris_test) # class (default type)
predict(iris_lda, type = "membership") # posterior probability
predict(iris_lda, type = "both") # both class and membership in a list
# Type projection
predict(iris_lda, type = "projection") # Projection on the LD axes
# Add test set items to the previous plot
points(predict(iris_lda, newdata = iris_test, type = "projection"),
  col = as.numeric(predict(iris_lda, newdata = iris_test)) + 1, pch = 19)
# predict() and confusion() should be used on a separate test set
# for unbiased estimation (or using cross-validation, bootstrap, ...)
# Wrong, cf. biased estimation (so-called, self-consistency)
confusion(iris_lda)
# Estimation using a separate test set
confusion(predict(iris_lda, newdata = iris_test), iris_test$Species)

# Another dataset (binary predictor... not optimal for lda, just for test)
data("HouseVotes84", package = "mlbench")
house_lda <- ml_lda(data = HouseVotes84, na.action = na.omit, Class ~ .)
summary(house_lda)
confusion(house_lda) # Self-consistency (biased metrics)
print(confusion(house_lda), error.col = FALSE) # Without error column

# More complex formulas
# Exclude one or more variables
iris_lda2 <- ml_lda(data = iris, Species ~ . - Sepal.Width)
summary(iris_lda2)
# With calculation
iris_lda3 <- ml_lda(data = iris, Species ~ log(Petal.Length) +
  log(Petal.Width) + I(Petal.Length/Sepal.Length))
summary(iris_lda3)

# Factor levels with missing items are allowed
ir2 <- iris[-(51:100), ] # No Iris versicolor in the training set
iris_lda4 <- ml_lda(data = ir2, Species ~ .)
summary(iris_lda4) # missing class
# Missing levels are reinjected in class or membership by predict()
predict(iris_lda4, type = "both")
# ... but, of course, the classifier is wrong for Iris versicolor
confusion(predict(iris_lda4, newdata = iris), iris$Species)

# Simpler interface, but more memory-effective
iris_lda5 <- ml_lda(train = iris[, -5], response = iris$Species)
summary(iris_lda5)