SL.qda | R Documentation |
Quadratic discriminant analysis, used for classification.
SL.qda(Y, X, newX, family, obsWeights = rep(1, nrow(X)), verbose = F,
id = NULL, 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 |
verbose |
If TRUE, display additional output during execution. |
id |
Not supported. |
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.
predict.SL.qda
qda
predict.qda
SL.lda
data(Boston, package = "MASS")
Y = as.numeric(Boston$medv > 23)
# Remove outcome from covariate dataframe.
X = Boston[, -14]
set.seed(1)
# Use only 2 CV folds to speed up example.
sl = SuperLearner(Y, X, family = binomial(), cvControl = list(V = 2),
SL.library = c("SL.mean", "SL.qda"))
sl
pred = predict(sl, X)
summary(pred$pred)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.