Implemented S3 methods for objects of class "cv.corila":
coef(): extracts estimated coefficients
predict(): calculates predicted values
fitted(): extracts fitted values
residuals(): calculates deviance residuals
plot(): visualises observed vs fitted values and estimated coefficients
print(): prints information to the console
summary(): summarises the fitted model
deviance(): extracts the deviance
nobs(): extracts the number of observations
Value
coef() returns a \((1 +) p\)-dimensional vector, predict() returns an \(n_1\)-dimensional vector, fitted() and residuals() return an \(n_0\)-dimensional vector. See individual methods for details.
See also
Use cv.corila() to fit the model.
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>
#> -0.24149791 0.00000000 -0.04191072 0.00000000 0.00000000 -0.32230634
#> <NA> <NA> <NA> <NA> <NA> <NA>
#> 0.00000000 -0.38686683 -0.22389187 0.00000000 -0.03457487 -0.08825507
#> <NA> <NA> <NA> <NA> <NA> <NA>
#> 0.00000000 -0.13812173 0.00000000 0.00000000 0.00000000 0.00000000
#> <NA> <NA> <NA>
#> 0.00000000 0.00000000 0.17883403
predict(object, newx = x)
#> [1] -0.62285965 -1.25759559 -1.25067536 -0.61376529 0.37283625 -0.45344181
#> [7] 0.71765790 0.27032945 -0.78264611 -0.02400301
fitted(object)
#> [1] -0.62285965 -1.25759559 -1.25067536 -0.61376529 0.37283625 -0.45344181
#> [7] 0.71765790 0.27032945 -0.78264611 -0.02400301
residuals(object)
#> [1] -0.208843194 0.066932493 -0.096722771 0.001584804 0.102326943
#> [6] -0.063491125 -0.014378403 0.013690764 0.075507917 0.123392572
plot(object)
print(object)
#> object of class ‘cv.corila’
#> (contains multiple objects of class ‘cv.glmnet’)
#> selected 8 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 = 0.04127
#> selected weights: local = 0.5, global = 0.5
#> selected exponents: local = 0, global = 1
#> 9 non-zero coefficients (including intercept)