SuperLearner (version 2.0-26)

SL.lda: 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

# NOT RUN {
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)

# }

Run the code above in your browser using DataCamp Workspace