Extracts the number of observations.
Usage
# S3 method for class 'cv.corila'
nobs(object, ...)Arguments
- object
object of class
"cv.corila"- ...
(for compatibility with stats::nobs)
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>
#> 0.10235956 0.00000000 0.00000000 0.00000000 0.00000000 -0.20630263
#> <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 0.01934724 0.00000000
predict(object, newx = x)
#> [1] 0.236149175 0.205475646 -0.194471656 0.231808305 0.158599736
#> [6] 0.205573215 0.121881660 0.171022293 0.007657089 0.130715548
fitted(object)
#> [1] 0.236149175 0.205475646 -0.194471656 0.231808305 0.158599736
#> [6] 0.205573215 0.121881660 0.171022293 0.007657089 0.130715548
residuals(object)
#> [1] -0.49508176 0.18890352 -0.65738544 2.41735858 -0.00258806 0.92463405
#> [7] -2.41100564 0.56997886 -1.32390225 0.78908813
plot(object)
print(object)
#> object of class ‘cv.corila’
#> (contains multiple objects of class ‘cv.glmnet’)
#> selected 2 from 20 predictors
summary(object)
#> --- object of class “cv.corila” ---
#> generalised linear model with gaussian family
#> 20 features (11 primary and 9 auxiliary features)
#> initial coefficients: ridge regression
#> final coefficients: adaptive lasso regression
#> optimised regularisation parameter: lambda.min = 0.6963
#> selected weights: local = 0, global = 1
#> selected exponents: local = Inf, global = 1
#> 3 non-zero coefficients (including intercept)