fl.lambda | R Documentation |

The function computes regression coefficients for a fused lasso penalized regression model for a given pair of lambda1 and lambda2 values.

fl.lambda(n,p,x,y,xpx,dxpx,xpy,beta.old,ofv.old,alpha, lambda1,lambda2,tol,maxiter,eps,xbeta.old)

`n` |
Number of observations |

`p` |
Number of predictors. |

`x` |
A n by l matrix of predictors. Here n is number of observations, l is number of active variables. |

`y` |
a vector of n observations. |

`xpx` |
The X'X matrix |

`dxpx` |
A vector of order l of diagonal elements of x'x |

`xpy` |
A vector of order l containing x'y |

`beta.old` |
A vector initial values of beta. Optional |

`ofv.old` |
Objective function value at beta.old |

`alpha` |
Approximation to be used for absolute value. Default is 10^-6. |

`lambda1` |
The value of lambda1 |

`lambda2` |
The value of lambda2 |

`tol` |
Tolerance criterion. Default is 10^-7 |

`maxiter` |
Maximum number of iterations. Default is 100000. |

`eps` |
Value for which beta is set to zero if -eps<beta<eps. Default is 10^-6 |

`xbeta.old` |
A n by 1 vector of xbeta values. Optional |

This function is internal and used by fusedlasso function. User need not call this function.

A list with following components

`beta.new` |
Coefficient estimates |

`conv` |
"yes" means converged and "no" means did not converge |

`iter` |
Number of iterations to estimate the coefficients |

`ofv.new` |
Objective function value at solution |

B N Mandal and Jun Ma

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