Description Usage Arguments Value Examples
Create an artificial classification problem with a W-shaped decision boundary.
1 2 3 4 5 6 7 | wData(n, d = 2, k = 1)
wLabels(data, k = 1)
wPosterior(data, k = 1)
wBayesClass(data, k = 1)
|
n |
Number of observations. |
d |
The dimensionality. |
k |
Parameter to adjust the noise level. |
data |
A |
wData
returns an object of class
"locClass"
, a list with components:
x |
(A matrix.) The explanatory variables. |
y |
(A factor.) The class labels. |
wLabels
returns a factor of class labels.
wPosterior
returns a matrix of posterior
probabilities.
wBayesClass
returns a factor of Bayes predictions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Generate a training and a test set
train <- wData(1000)
test <- wData(1000)
# Generate a grid of points
x.1 <- x.2 <- seq(0.01,1,0.01)
grid <- expand.grid(x.1 = 3 * x.1, x.2 = x.2)
# Calculate the posterior probablities for all grid points
gridPosterior <- wPosterior(grid)
# Draw contour lines of posterior probabilities and plot training observations
contour(x.1, x.2, matrix(gridPosterior[,1], length(x.1)), col = "gray")
points(train$x, col = train$y)
# Calculate Bayes error
ybayes <- wBayesClass(test$x)
mean(ybayes != test$y)
if (require(MASS)) {
# Fit an LDA model and calculate misclassification rate on the test data set
tr <- lda(y ~ ., data = as.data.frame(train))
pred <- predict(tr, as.data.frame(test))
mean(pred$class != test$y)
# Draw decision boundary
gridPred <- predict(tr, grid)
contour(x.1, x.2, matrix(gridPred$posterior[,1], length(x.1)), col = "red", levels = 0.5, add = TRUE)
}
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.