stability.HCgglasso: Stability Selection

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/stability.R

Description

Stability selection for HCgglasso

Usage

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stability.HCgglasso(X, y, B = 50, fraction = 0.5, hc = NULL,
  lambda = NULL, weightLevel = NULL, weightSizeGroup = NULL,
  intercept = TRUE, verbose = FALSE, ...)

Arguments

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 B sub-samples

hc

output of hclust function. If not provided, hclust is run with ward.D2 method

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 gglasso function

Value

a stability.HCgglasso object containing :

lambda

sequence of lambda.

B

Number of bootstrap samples.

stability

A matrix of size length(lambda)*number of groups containing the probability of selection of each group

var

vector containing the index of covariates

group

vector containing the index of associated groups of covariates

time

computation time

Author(s)

Quentin Grimonprez

References

Meinshausen and Buhlmann (2010). Stability selection. In : Journal of the Royal Statistical Society : Series B (Statistical Methodology) 72.4, p. 417-473.

See Also

cv.HCgglasso, HCgglasso

Examples

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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)

HCgglasso documentation built on May 2, 2019, 4:54 p.m.