errorrate function

Error rate of the Bayes rule for two-class Gaussian homoscedastic model

The optimal error rate of Bayes rule for two-class Gaussian homoscedastic model

errorrate(beta0, beta, pi, mu, sigma)

Arguments

• beta0: An $n\times p$ matrix where each row represents an individual observation
• beta: Number of observations.
• pi: A g-dimensional vector for the initial values of the mixing proportions.
• mu: A $p \times g$ matrix for the initial values of the location parameters.
• sigma: A $p\times p$ covariance matrix if ncov=1, or a list of g covariance matrices with dimension $p\times p \times g$ if ncov=2.

Returns

• errval: A vector of error rate.

Details

The optimal error rate of Bayes rule for two-class Gaussian homoscedastic model can be expressed as

where $\phi$ is a normal probability function with mean $\mu_i$ and covariance matrix $\Sigma_i$.