coef.cv.nc.hdqr {hdqr} | R Documentation |
Extract Coefficients from a 'cv.nc.hdqr' Object
Description
Retrieves coefficients at specified values of 'lambda' from a fitted 'cv.nc.hdqr()' model. Utilizes the stored '"nchdqr.fit"' object and the optimal 'lambda' values determined during the cross-validation process.
Usage
## S3 method for class 'cv.nc.hdqr'
coef(object, s = c("lambda.1se", "lambda.min"), ...)
Arguments
object |
A fitted 'cv.nc.hdqr()' object from which coefficients are to be extracted. |
s |
Specifies the 'lambda' values at which coefficients are requested. The default is 's = "lambda.1se"', representing the largest 'lambda' such that the cross-validation error estimate is within one standard error of the minimum. Alternatively, 's = "lambda.min"' corresponds to the 'lambda' yielding the minimum cross-validation error. If 's' is numeric, these values are directly used as the 'lambda' values for coefficient extraction. |
... |
Not used. |
Value
Returns a vector or matrix of coefficients corresponding to the specified 'lambda' values.
See Also
cv.nc.hdqr
, predict.cv.nc.hdqr
Examples
set.seed(315)
n <- 100
p <- 400
x <- matrix(data = rnorm(n * p, mean = 0, sd = 1), nrow = n, ncol = p)
beta_star <- c(c(2, 1.5, 0.8, 1, 1.75, 0.75, 0.3), rep(0, (p - 7)))
eps <- rnorm(n, mean = 0, sd = 1)
y <- x %*% beta_star + eps
tau <- 0.5
lam2 <- 0.01
lambda <- 10^(seq(1,-4, length.out=30))
cv.nc.fit <- cv.nc.hdqr(x = x, y = y, tau = tau, lambda = lambda, lam2 = lam2)
coef(cv.nc.fit, s = c(0.02, 0.03))