qda: Quadratic discrimination

In msos: Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden

Description

The function returns the elements needed to calculate the quadratic discrimination in (11.48). Use the output from this function in predict_qda (Section A.3.2) to find the predicted groups.

Usage

qda(x, y)

Arguments

x	The N x P data matrix.
y	The N-vector of group identities, assumed to be given by the numbers 1,...,K for K groups.

Value

A 'list' with the following components:

Mean

A P x K matrix, where column K contains the coefficents a_k for (11.31). The final column is all zero.

Sigma

A K x P x P array, where the Sigma[k,,] contains the sample covariance matrix for group k, \hat{Σ_k}.

c

The K-vector of constants c_k for (11.48).

See Also

predict_qda and lda

Examples

# Load Iris Data
data(iris)

# Iris example
x.iris <- as.matrix(iris[, 1:4])

# Gets group vector (1, ... , 1, 2, ... , 2, 3, ... , 3)
y.iris <- rep(1:3, c(50, 50, 50))

# Perform QDA
qd.iris <- qda(x.iris, y.iris)

msos documentation built on Oct. 31, 2020, 9:07 a.m.