pense (version 1.2.9)

residuals.elnetfit: Extract Residuals from a Fitted Elastic-Net Estimator

Description

Extract Residuals from a Fitted Elastic-Net Estimator

Usage

# S3 method for elnetfit
residuals(object, lambda, exact = FALSE, ...)

Arguments

object

a elnet estimate to extract the residuals from.

lambda

the value of the penalty parameter. Default is to use the optimal lambda object$lambda_opt if the given estimator was cross-validated or the smallest lambda if not.

exact

if the lambda is not part of the lambda grid, should the estimates be obtained by linear interpolation between the nearest lambda values (default) or computed exactly.

...

currently ignored.

Value

a numeric vector of residuals for the given lambda.

Examples

# NOT RUN {
# Generate data with highly correlated groups of variables
set.seed(12345)
n <- 100
p <- 20
x <- 1 + matrix(rnorm(n * p), ncol = p)
x[, 2] <- x[, 1] + rnorm(n, sd = 0.01)
x[, 3] <- x[, 1] + rnorm(n, sd = 0.01)
x[, 5] <- x[, 4] + rnorm(n, sd = 0.01)
x[, 6] <- x[, 4] + rnorm(n, sd = 0.01)

y <- drop(x %*% c(rep(c(2, 5), each = 3), numeric(p - 6)) + rnorm(n))

# Compute the classical EN and select the optimal lambda by CV
set.seed(1234)
est_en_cv <- elnet_cv(
    x, y,
    alpha = 0.5,
    correction = TRUE
)

# For cross-validated EN fits, the `coef`, `predict`, and `residuals` methods
# return/use the estimated coefficients at the "optimal" lambda
coef(est_en_cv) # Extract coefficients
predict(est_en_cv) # Extract fitted values
predict(est_en_cv, newdata = x) # Predict values
residuals(est_en_cv) # Extract residuals

# We can also request the coefficient at another lambda. By default,
# this will interpolate between the solutions at the two surrounding
# lambda values.
coef(est_en_cv, lambda = 6)

# If needed, the solution at the given lambda can also be computed exactly.
coef(est_en_cv, lambda = 6, exact = TRUE)


# If we compute the EN estimator without choosing an optimal lambda,
# the lambda parameter needs to be specified in the call to coef
est_en <- elnet(
    x, y,
    alpha = 0.5
)

# Without specifying lambda, the `coef` method would raise an error.
coef(est_en, lambda = 6)
# }