glmmLasso fitThe values supplied in the function call replace the defaults and a list with all possible arguments is returned. The returned list is used as the control argument to the glmmLasso function.
glmmLassoControl(nue=1,index=NULL,smooth=NULL, start=NULL, q_start=NULL,
center = TRUE, standardize = TRUE, steps=1000,
method="EM", overdispersion=FALSE,
epsilon=1e-4, maxIter=200, print.iter=FALSE,
print.iter.final=FALSE, method.final="EM",
eps.final=1e-4, Q.fac=5, complexity="hat.matrix",...)a list with components for each of the possible arguments.
weakness of the learner. Choose 0 < nue =< 1. Default is 1.
vector which defines the grouping of the variables. Components sharing the same number build a group and factor variables get a single number (and are automatically treated as a group). Non-penalized coefficients are marked with NA.
a list specifying the formula of the smooth terms, together with the number of basis functions nbasis, the degree of the B-splines spline.degree, the order of differences that is used for penalization diff.ord and finally a correspodning penalty parameter penal.
a vector containing starting values for fixed and random effects of suitable length. Default is a vector full of zeros.
a scalar or matrix of suitable dimension, specifying starting values for the random-effects variance-covariance matrix. Default is a scalar 0.1 or diagonal matrix with 0.1 in the diagonal, depending on the dimension of the random effects.
logical. If true, the columns of the design matrix will be centered (except a possible intercept column).
logical. If true, the design matrix will be blockwise orthonormalized such that for each block \(X^TX = n 1\) (*after* possible centering).
the number of iterations. Default is 1000.
two methods for the computation of the random-effects variance-covariance parameter estimates can be chosen, an EM-type estimate and an REML-type estimate. The REML-type estimate uses the nlminb or the bobyqa function for optimization, depending on the dimension of the random effects. Default is EM.
logical scalar. If FALSE, no scale parameter is derived, if TRUE, in each iteration a scale parameter is estimated by use of Pearson residuals.
This can be used e.g. to fit overdispersed Poisson models. Default is FALSE.
If the Gaussian family is used, overdispersion is automatically set TRUE.
controls the speed of convergence. Default is 1e-4.
the number of iterations for the final Fisher scoring re-estimation procedure. Default is 200.
logical. Should the number of iterations be printed? Default is FALSE.
logical. Should the number of iterations in the final re-estimation step be printed? Default is FALSE.
two methods for the computation of the random-effects variance-covariance parameter estimates
for the final Fisher scoring re-estimation procedure can be chosen, an EM-type estimate and an REML-type estimate. The REML-type estimate uses the bobyqa function for optimization.
Default is EM.
controls the speed of convergence in the final re-estimation. Default is 1e-4.
Factor which controls the interval on which is searched for the optimal parameters of the random-effects variance-covariance matrix, if method.final="REML". Default is 5.
Character which determines how the model complexity is computed. Default is "hat.matrix", which sums up the trace of the corresponding hat matrix. Alternatively, simply the number of estimated (non-zero) parameters can be used by setting complexity="non-zero".
Futher arguments to be passed.
Andreas Groll groll@statistik.tu-dortmund.de
glmmLasso, bobyqa
# Use REML estimates for random effects covariance parameters
# and lighten the convergence criterion
glmmLassoControl(method="REML", epsilon=1e-4)
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