Skip to contents

Summarising sparse group lasso (S3 method)

Source: R/methods.R
summary.cv.corila.Rd

Summary method for class "cv.corila".

Usage

# S3 method for class 'cv.corila'
summary(object, ...)

# S3 method for class 'summary.cv.corila'
print(x, ...)

Arguments

object

object of class "cv.corila"

...

(for compatibility with base::summary)

x

object of class "summary.cv.corila"

Value

Returns an invisible list with multiple slots.

Details

print.summary.cv.corila() uses the output from summary.cv.corila() to print readable information to the console. It calls the helper function .type() to name the methods used to estimate initial and final coefficients.

See also

print.cv.corila()

Examples

n <- 12L # decrease to 10 to check LOOCV
p <- 20L
q <- 5L
x <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- rnorm(n)
group <- rep(seq_len(q), length.out = p)
primary <- as.logical(rbinom(n = p, size = 1L, prob = 0.5))
object <- cv.corila(x = x, y = y, group = group, primary = primary)
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
print(object)
#> object of class ‘cv.corila’ 
#> (contains multiple objects of class ‘cv.glmnet’)
#> selected 1 from 20 predictors
summary(object)
#> --- object of class “cv.corila” --- 
#> generalised linear model with gaussian family 
#> 20 features (9 primary and 11 auxiliary features)
#> initial coefficients: ridge regression 
#> final coefficients: adaptive lasso regression 
#> optimised regularisation parameter: lambda.min = 0.9959 
#> selected weights: local = 0.1, global = 0.9
#> selected exponents: local = 0, global = 1
#> 2 non-zero coefficients (including intercept)