lambdalasso {ggmix}

Estimation of Lambda Sequence for Linear Mixed Model with Lasso Penalty

Description

lambdalasso estimates a decreasing sequence of tuning parameters

Usage

lambdalasso(ggmix_object, ...)

## Default S3 method:
lambdalasso(ggmix_object, ...)

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

Arguments

ggmix_object

A ggmix_object object of class lowrank or fullrank

...

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.

Value

A decreasing sequence of tuning parameters

Note

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

See Also

ggmix