Description Usage Arguments Value Author(s) References See Also Examples
Stability selection for HCgglasso
1 2 3 |
X |
matrix of size n*p |
y |
vector of size n |
B |
number of bootstrap sample |
fraction |
Fraction of data used at each of the |
hc |
output of |
lambda |
lambda values for group lasso. If not provided, the function generates its own values of lambda |
weightLevel |
a vector of size p for each level of the hierarchy. A zero indicates that the level will be ignored. If not provided, use 1/(height between 2 successive levels) |
weightSizeGroup |
a vector |
intercept |
should an intercept be included in the model ? |
verbose |
print some informations |
... |
Others parameters for |
a stability.HCgglasso object containing :
sequence of lambda
.
Number of bootstrap samples.
A matrix of size length(lambda)*number of groups containing the probability of selection of each group
vector containing the index of covariates
vector containing the index of associated groups of covariates
computation time
Quentin Grimonprez
Meinshausen and Buhlmann (2010). Stability selection. In : Journal of the Royal Statistical Society : Series B (Statistical Methodology) 72.4, p. 417-473.
cv.HCgglasso, HCgglasso
1 2 3 4 | set.seed(42)
X = simuBlockGaussian(50,12,5,0.7)
y = drop(X[,c(2,7,12)]%*%c(2,2,-2)+rnorm(50,0,0.5))
res = stability.HCgglasso(X,y)
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