Description Usage Arguments Details Value Author(s) References Examples

To perform two methods, Kappa and PASS, for selecting tuning parameters in regularized procedures such as LASSO, SCAD, and adaptive LASSO

1 2 3 4 5 6 |

`data` |
It is an n by (p+1) matrix, where the first p columns form the design matrix and the last column is response vector. |

`base` |
It is the base procedure used for variable selection. Three choices of base are "LASSO", "SCAD", and "aLASSO". |

`lambda.grid` |
It is a vector consisting of the values of tuning parameter lambda to be evaluated. If lambda.grid=NULL, a grid of lambda's will be decided automatically, with specified number of lambda's to be considered. |

`num.grid` |
It is the number of lambda's to be considered, where a grid of lambda's is decided manually or automatically. The default value is 20. |

`num.split` |
It is the number of random half-half splittings. The default value is 20. |

`alpha` |
It is the threshold only used for the Kappa selection method. It is not a tuning parameter. The default value is 0.1. |

`x` |
This is the output object of class |

`...` |
Not used. |

Because the data matrix will be centerized so that the column means are zero, there is no need an intercept column in the data matrix.
Function `print.lass(x)`

prints the two estimated optimal values of tuning parameter lambda and function `plot.lass(x)`

plots
the two tuning parameter selection processess, where `x`

is the output of function `pass`

.

`pass.values` |
The values evaluated over lambda.grid using the PASS criterion. A curve based on these values can be drawn using function |

`kappa.values` |
The values evaluated over lambda.grid using the Kappa criterion. A curve based on these values can be drawn using function |

`lambda.pass` |
The estimated optimal value for the tuning parameter lambda using the PASS criterion |

`lambda.kappa` |
The estimated optimal value for the tuning parameter lambda using the Kappa criterion (adjusted for the threshold |

`beta.pass` |
The estimated coefficients using selected lambda by the PASS criterion |

`beta.kappa` |
The estimated coefficients using selected lambda by the Kappa criterion (adjusted for the threshold |

`subset.pass` |
The selected submodel by the PASS criterion |

`subset.kappa` |
The selected submodel by the Kappa criterion (adjusted for the threshold |

Yixin Fang, Wei Sun, Junhui Wang

(1) Sun, Wang, and Fang (2012+) Consistent selection of tuning parameters via variable selection stability. Revision Submmitted. Available at arXiv.

(2) Fang, Wang, and Sun (2012+) A PASS for tuning parameter selection in regularized regression. Submmitted. Available at arXiv.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
library(MASS)
library(lars)
library(ncvreg)
beta=c(3,1.5,0,0,2,0,0,0)
p=8
n=100
sigma=1
rho=0.5
set.seed(100)
x=matrix(0, n, p)
x[,1]<-rnorm(n, 0, 1)
for (i in 2:p) x[,i]<-rho*x[,i-1]+sqrt(1-rho^2)*rnorm(n, 0, 1)
y=x%*%beta+sigma*rnorm(n, 0, 1)
data<-cbind(x,y)
lambda.grid=10^seq(-2,2,length=20)
results<-pass(data=data, base="LASSO", lambda.grid=lambda.grid, num.grid=20, num.split=20)
print(results)
plot(results)
``` |

pass documentation built on May 30, 2017, 3:33 a.m.

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