SL.lda: SL wrapper for MASS:lda

In SuperLearner: Super Learner Prediction

SL wrapper for MASS:lda

Description

Linear discriminant analysis, used for classification.

Usage

SL.lda(Y, X, newX, family, obsWeights = rep(1, nrow(X)), id = NULL,
  verbose = F, prior = as.vector(prop.table(table(Y))), method = "mle",
  tol = 1e-04, CV = F, nu = 5, ...)

Arguments

Y	Outcome variable
X	Training dataframe
newX	Test dataframe
family	Binomial only, cannot be used for regression.
obsWeights	Observation-level weights
id	Not supported.
verbose	If TRUE, display additional output during execution.
prior	the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels.
method	"moment" for standard estimators of the mean and variance, "mle" for MLEs, "mve" to use cov.mve, or "t" for robust estimates based on a t distribution.
tol	tolerance
CV	If true, returns results (classes and posterior probabilities) for leave-one-out cross-validation. Note that if the prior is estimated, the proportions in the whole dataset are used.
nu	degrees of freedom for method = "t".
...	Any additional arguments, not currently used.

References

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (Vol. 6). New York: Springer. Section 4.4.

See Also

predict.SL.lda lda predict.lda SL.qda

Examples

data(Boston, package = "MASS")
Y = as.numeric(Boston$medv > 23)
# Remove outcome from covariate dataframe.
X = Boston[, -14]

set.seed(1)

# Use only 2 CV folds to speed up example.
sl = SuperLearner(Y, X, family = binomial(), cvControl = list(V = 2),
                 SL.library = c("SL.mean", "SL.lda"))
sl

pred = predict(sl, X)
summary(pred$pred)

SuperLearner documentation built on May 29, 2024, 5:25 a.m.