Extracts fitted values.
Usage
# S3 method for class 'cv.corila'
fitted(object, ...)Arguments
- object
object of class
"cv.corila"- ...
(for compatibility with stats::fitted)
See also
Use predict() to obtain predicted values (i.e., for testing observations).
Examples
# listing S3 methods
methods(class = "cv.corila")
#> [1] coef deviance fitted nobs plot predict print
#> [8] residuals summary
#> see '?methods' for accessing help and source code
# simulating data
n <- 10L; 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))
# fitting the model
object <- cv.corila(x = x, y = y, group = group, primary = primary)
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
# using S3 methods
coef(object)
#> (intercept) <NA> <NA> <NA> <NA> <NA>
#> 0.06401393 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> <NA> <NA> <NA> <NA> <NA> <NA>
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> <NA> <NA> <NA> <NA> <NA> <NA>
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> <NA> <NA> <NA>
#> 0.00000000 0.00000000 0.00000000
predict(object, newx = x)
#> [1] 0.06401393 0.06401393 0.06401393 0.06401393 0.06401393 0.06401393
#> [7] 0.06401393 0.06401393 0.06401393 0.06401393
fitted(object)
#> [1] 0.06401393 0.06401393 0.06401393 0.06401393 0.06401393 0.06401393
#> [7] 0.06401393 0.06401393 0.06401393 0.06401393
residuals(object)
#> [1] -0.4899953 0.9326448 0.6636468 -1.7906445 0.2893846 0.6627997
#> [7] 0.6042470 -2.4883312 -0.2993714 1.9156194
plot(object)
print(object)
#> object of class ‘cv.corila’
#> (contains multiple objects of class ‘cv.glmnet’)
#> selected 0 from 20 predictors
summary(object)
#> --- object of class “cv.corila” ---
#> generalised linear model with gaussian family
#> 20 features (10 primary and 10 auxiliary features)
#> initial coefficients: ridge regression
#> final coefficients: adaptive lasso regression
#> optimised regularisation parameter: lambda.min = 1.166
#> selected weights: local = 1, global = 0
#> selected exponents: local = 0, global = Inf
#> 1 non-zero coefficients (including intercept)