Stability selection for MLGL
1 2 3 
X 
matrix of size n*p 
y 
vector of size n. If loss = "logit", elements of y must be in 1,1 
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 
loss 
a character string specifying the loss function to use, valid options are: "ls" least squares loss (regression) and "logit" logistic loss (classification) 
intercept 
should an intercept be included in the model ? 
verbose 
print some informations 
... 
Others parameters for 
Hierarhical clustering is performed with all the variables. Then, the partitions from the different levels of the hierarchy are used in the differents run of MLGL for estimating the probability of selection of each group.
a stability.MLGL 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. 417473.
cv.MLGL, MLGL
1 2 3 4 5 6 7 8 9 10  ## Not run:
set.seed(42)
# Simulate gaussian data with blockdiagonal variance matrix containing 12 blocks of size 5
X < simuBlockGaussian(50, 12, 5, 0.7)
# Generate a response variable
y < drop(X[,c(2,7,12)]%*%c(2,2,2)+rnorm(50,0,0.5))
# Apply stability.MLGL method
res < stability.MLGL(X,y)
## End(Not run)

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