SL wrapper for MASS:lda
Linear discriminant analysis, used for classification.
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, ...)
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. |
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (Vol. 6). New York: Springer. Section 4.4.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.