`R/lambda.R`

`lambdalasso.Rd`

`lambdalasso`

estimates a decreasing sequence of tuning
parameters

lambdalasso(ggmix_object, ...) # S3 method for default lambdalasso(ggmix_object, ...) # S3 method for fullrank lambdalasso( ggmix_object, ..., penalty.factor, lambda_min_ratio, epsilon = 1e-14, tol.kkt = 1e-09, eta_init = 0.5, nlambda = 100, scale_x = F, center_y = F )

ggmix_object | A ggmix_object object of class |
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... | Extra parameters. Currently ignored. |

penalty.factor | Separate penalty factors can be applied to each coefficient. This is a number that multiplies lambda to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables |

lambda_min_ratio | Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero). The default depends on the sample size nobs relative to the number of variables nvars. If nobs > nvars, the default is 0.0001, close to zero. If nobs < nvars, the default is 0.01. A very small value of lambda.min.ratio will lead to a saturated fit in the nobs < nvars case. |

epsilon | Convergence threshold for block relaxation of the entire parameter vector \(\Theta = ( \beta, \eta, \sigma^2 )\). The algorithm converges when $$crossprod(\Theta_{j+1} - \Theta_{j}) < \epsilon$$. Defaults value is 1E-4 |

tol.kkt | KKT tolerance. Currently ignored |

eta_init | initial value for the eta parameter, with \(0 < \eta < 1\) used in determining lambda.max and starting value for fitting algorithm. |

nlambda | the number of lambda values - default is 100. |

scale_x | should the columns of x be scaled - default is FALSE |

center_y | should y be mean centered - default is FALSE. |

A decreasing sequence of tuning parameters

This function isn't meant to be called directly by the user.