This function calculate the sequential, parametric bootstrap and perturbation instability measures for linear regression with Lasso, SCAD and MCP penalty.
1 2 3 4 5 
x 
Matrix of predictors. 
y 
Response variable. 
method 
Type of instability measures. 
penalty 
Penalty function. 
nrep 
Number of repetition for calculating instability, default is 50. 
remove 
The portion of observation to be removed when the sequential instability is calculated, default is 0.2. 
tau 
The size of perturbation when perturbation instability is calculated. The range of 
nfolds 
number of folds  default is 5. 
family 
Choose the family for the instability test. So far only 
See Reference section.
Return the instability index according to the type of instability measures.
Nan, Y. and Yang, Y. (2013), "Variable Selection Diagnostics Measures for Highdimensional Regression," Journal of Computational and Graphical Statistics, 23:3, 636656.
http://dx.doi.org/10.1080/10618600.2013.829780
BugReport: https://github.com/emeryyi/glmvsd
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  # generate simulation data
n < 50
p < 8
beta<c(2.5,1.5,0.5,rep(0,5))
sigma<matrix(0,p,p)
for(i in 1:p){
for(j in 1:p) sigma[i,j] < 0.5^abs(ij)
}
x < mvrnorm(n, rep(0,p), sigma)
e < rnorm(n)
y < x %*% beta + e
ins_seq < stability.test(x, y, method = "seq",
penalty = "SCAD", nrep = 20,
remove = 0.1, tau = 0.2, nfolds = 5)

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