lmmlassoControl: Options for the lmmlasso Algorithm
In lmmlasso: Linear mixed-effects models with Lasso

Description Usage Arguments Details References

Definition of various kinds of options in the algorithm.

 lmmlassoControl(tol = 10^(-4), trace = 1, maxIter = 1000,
 maxArmijo = 20, number = 5, a_init = 1, delta = 0.1, rho = 0.001,
 gamma = 0, lower = 10^(-6), upper = 10^8, seed = 532,
 VarInt = c(0, 10), CovInt = c(-5, 5), thres = 10^(-4))

`tol`	convergence tolerance
`trace`	integer. 1 prints no output, 2 prints warnings, 3 prints the current function values and warnings (not recommended)
`maxIter`	maximum number of (outer) iterations
`maxArmijo`	maximum number of steps to be chosen in the Armijo Rule. If the maximum is reached, the algorithm continues with optimizing the next coordinate.
`number`	integer. Determines the active set algorithm. The zero fixed-effects coefficients are only updated each number iteration. It may be that a smaller number increases the speed of the algorithm. Use 0 ≤ number ≤ 5.
`a_init`	α_{init} in the Armijo step. See Schelldorfer et. al. (2010).
`delta`	δ in the Armijo step. See Schelldorfer et. al. (2010)
`rho`	ρ in the Armijo step. See Schelldorfer et. al. (2010)
`gamma`	γ in the Armijo step. See Schelldorfer et. al. (2010)
`lower`	lower bound for the Hessian
`upper`	upper bound for the Hessian
`seed`	set.seed for calculating the starting value, which performs a 10-fold cross-validation.
`VarInt`	Only for opt="optimize". The interval for the variance parameters used in "optimize". See help("optimize")
`CovInt`	Only for opt="optimize". The interval for the covariance parameters used in "optimize". See help("optimize")
`thres`	If a variance or covariance parameter has smaller absolute value than thres, the parameter is set to exactly zero.

For the Armijo step parameters, see Bertsekas (2003)

Dimitri P. Bertsekas (2003) Nonlinear Programming, Athena Scientific.

J. Schelldorfer, P. B\"uhlmann and S. van de Geer (2011), Estimation for High-Dimensional Linear Mixed-Effects Models Using \ell_1-penalization, arXiv preprint 1002.3784v2

lmmlasso documentation built on May 2, 2019, 2:16 p.m.