controlGlmnet {lessSEM}

controlGlmnet

Description

Control the GLMNET optimizer.

Usage

controlGlmnet(
  startingValues = "est",
  initialHessian = ifelse(all(startingValues == "est"), "lavaan", "compute"),
  saveDetails = FALSE,
  stepSize = 0.9,
  sigma = 1e-05,
  gamma = 0,
  maxIterOut = 1000,
  maxIterIn = 1000,
  maxIterLine = 500,
  breakOuter = 1e-08,
  breakInner = 1e-10,
  convergenceCriterion = 0,
  verbose = 0,
  nCores = 1
)

Arguments

startingValues

option to provide initial starting values. Only used for the first lambda. Three options are supported. Setting to "est" will use the estimates from the lavaan model object. Setting to "start" will use the starting values of the lavaan model. Finally, a labeled vector with parameter values can be passed to the function which will then be used as starting values.

initialHessian

option to provide an initial Hessian to the optimizer. Must have row and column names corresponding to the parameter labels. use getLavaanParameters(lavaanModel) to see those labels. If set to "gradNorm", the maximum of the gradients at the starting values times the stepSize will be used. This is adapted from Optim.jl https://github.com/JuliaNLSolvers/Optim.jl/blob/f43e6084aacf2dabb2b142952acd3fbb0e268439/src/multivariate/solvers/first_order/bfgs.jl#L104 If set to "compute", the initial hessian will be computed. If set to a single value, a diagonal matrix with the single value along the diagonal will be used. The default is "lavaan" which extracts the Hessian from the lavaanModel. This Hessian will typically deviate from that of the internal SEM represenation of lessSEM (due to the transformation of the variances), but works quite well in practice.

saveDetails

when set to TRUE, additional details about the individual models are save. Currently, this are the Hessian and the implied means and covariances. Note: This may take a lot of memory!

stepSize

Initial stepSize of the outer iteration (theta_next = theta_previous + stepSize * Stepdirection)

sigma

only relevant when lineSearch = 'GLMNET'. Controls the sigma parameter in Yuan, G.-X., Ho, C.-H., & Lin, C.-J. (2012). An improved GLMNET for l1-regularized logistic regression. The Journal of Machine Learning Research, 13, 1999–2030. https://doi.org/10.1145/2020408.2020421.

gamma

Controls the gamma parameter in Yuan, G.-X., Ho, C.-H., & Lin, C.-J. (2012). An improved GLMNET for l1-regularized logistic regression. The Journal of Machine Learning Research, 13, 1999–2030. https://doi.org/10.1145/2020408.2020421. Defaults to 0.

maxIterOut

Maximal number of outer iterations

maxIterIn

Maximal number of inner iterations

maxIterLine

Maximal number of iterations for the line search procedure

breakOuter

Stopping criterion for outer iterations

breakInner

Stopping criterion for inner iterations

convergenceCriterion

which convergence criterion should be used for the outer iterations? possible are 0 = GLMNET, 1 = fitChange, 2 = gradients. Note that in case of gradients and GLMNET, we divide the gradients (and the Hessian) of the log-Likelihood by N as it would otherwise be considerably more difficult for larger sample sizes to reach the convergence criteria.

verbose

0 prints no additional information, > 0 prints GLMNET iterations

nCores

number of core to use. Multi-core support is provided by RcppParallel and only supported for SEM, not for general purpose optimization.

Value

object of class controlGlmnet

Examples

control <- controlGlmnet()