se(beta_j) = sqrt(sigma^2 v_jj) with sigma^2 = RSS(beta) / (n - p) at the
supplied beta (ESL eq 3.8).
Usage
morie_esl_se_beta(X, y, beta)
Arguments
- X
Design matrix, n > p.
- y
Response.
- beta
Coefficients.
Value
Named list: estimate, se, sigma2_hat, rss, df_residual, n, p.
References
Hastie, Tibshirani & Friedman (2009), sec. 3.2.
Examples
morie_esl_se_beta(cbind(1, c(1, -1, 1, -1)), c(3, -1, 3, -1), c(0, 2))$sigma2_hat
#> [1] 2
