msos (version 1.1.1)

qda: Quadratic discrimination

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 $N\times P$ 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 $P\times K$ matrix, where column $K$ contains the coefficents $a_k$ for (11.31). The final column is all zero.

Sigma

A $K\times P\times P$ array, where the Sigma[k,,] contains the sample covariance matrix for group $k$, $\widehat{{\mathrm{\Sigma }}_k}$.

c

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

See Also

predict_qda and lda

Examples

# NOT RUN {
# 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)
# }