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 High-dimensional Regression," *Journal of Computational and Graphical Statistics*, 23:3, 636-656.

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(i-j)
}
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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