qda function

Quadratic discrimination

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.

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.

Returns

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 $k$, $\hat{\Sigma_k}$.
• c: The $K$-vector of constants $c_k$ for (11.48).

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)

See Also

predict_qda and lda