Fits the solution for a highdimensional generalized linear mixed model.
1 2 3 4 5 6 7 8 9  glmmlasso(y,...)
## Default S3 method:
glmmlasso(y, group, X, Z = NULL,
family = c("binomial", "poisson"),
covStruct = c("Identity", "Diagonal", "Sym"),
lambda, weights = NULL, coefInit = NULL, exactStep = FALSE,
exactDeriv = FALSE, random = NULL, unpenalized = 1, ranInd = 1,
control = glmmlassoControl(family = family), ...)

y 
response variable of length n. 
group 
grouping variable of length n. 
X 
fixedeffects matrix as an n x p matrix. An intercept has to be included in the first column as (1,...,1). 
Z 
randomeffects matrix as an n x q matrix, must be in sparse Matrix format (see package Matrix). 
family 
GLM family. Currently only "binomial" and "poisson" are implemented. 
covStruct 
Covariance Stucture to be used. "Identity" fits \bm{Σ}_{θ}=θ^2\bm{1}_q, i.e. one single covariance parameter for all random effects. "Diagonal" fits a diagonal matrix for \bm{Σ}_{θ}, i.e. for each random effect a different covariance parameter. "Sym" fits an unstructured symmetric matrix. 
lambda 
nonnegative regularization parameter 
weights 
weights for the fixedeffects covariates: NA means no penalization, 0 means drop this covariate ; if given, the argument unpenalized is ignored. By default each covariate has weight 1 
coefInit 
starting values. They must be of the same length as the model to be fitted, i.e. the number of variables p, the number of randomeffects q and the number of covariance parameters stot must coincide. Otherwise a warning is issued. 
exactStep 
logical. Should the Armijo step include the update of the random effects u to ensure that the objective function strictly decreases? 
exactDeriv 
logical. Should the exact derivate be calculated or the derivative for fixed random effects u. 
random 
expression for the randomeffects structure for noncorrelated random effects of the form "(1group)+(0+X2group)+(0+X3group)". It is used only for generating the corresponding Z matrix, and it dominates the Z matrix, i.e. if random and a Z matrix is given, the Z matrix corresponding to random is used. 
unpenalized 
indices as subset of \{1,...,p\} indicating which fixedeffects covariates are not subject to the \ell_1penalty. Ignored if weights is given. 
ranInd 
indices of the random effects as subset of
\{1,...,p\}. Only used for

control 
control parameters for the algorithm, see

... 
not used. 
All the details of the algorithm can be found in the article.
Concerning logLik, deviance, aic and bic, we have to be very careful when
comparing them with other generalized linear mixed model
functions. If we study a lowdimensional data example and set
λ=0, the loglikelihood is calculated as given in the
paper. Deviance, aic and bic are computed such that they coincide with
the results from glmer
from the lme4
package. The latter
does not employ the standard definitions of deviance and
loglikelihood function value. Nevertheless, the differences only
depends on constants, and not the parameters.
A glmmlasso
object is returned, for which
coef
,resid
, fitted
, logLik
print
, summary
, plot
methods exist.
fixef 
fixedeffects parameter beta 
coefficients 
fixedeffects parameter beta 
theta 
covariance parameter estimates 
ranef 
random effects in sparse vector format 
u 
random effects in dense vector format 
objective 
Value of the objective function corresponding to the estimates 
logLik 
value of the loglikelihood function. See details. 
deviance 
value of the deviance function. See details. 
aic 
AIC. See details. 
bic 
BIC. See details. 
activeSet 
Indices of the nonzero fixedeffects coefficients. 
eta 
The linear predictor at the current values. 
mu 
The conditional expectation of the response at the current values. 
fitted 
The fitted values at the current values. 
lambda 
nonnegative regularization parameter 
weights 
weights (possible adapted to the argument weights) 
data 
data. List with y, group, X and Z 
family 
GLM family used. 
ntot 
total number of observations 
p 
number of fixedeffects parameters 
N 
number of groups/clusters 
unpenalized 
indices of the nonpenalized fixedeffects covariates 
ranInd 
indices of the random effects as subset of \{1,...,p\}. 
exactStep 
logical. If the Armijo step includes the update of the random effects u or not. 
exactDeriv 
logical. If the exact derivate has been calculated or if the derivative for fixed random effects u has been calculated. 
coefInit 
starting values used in the algorithm for beta, theta and u 
coefOut 
list with estimates in the form required for the argument coefInit 
convergence 
integer giving convergence information. Each time maxArmijo was reached, convergence is increased by 2. If maxIter was reached, convergence is increased by 1. 
nIter 
number of outer iterations. 
nIterPirls 
number of pirls evaluation within the outer iteration. PirlsEvaluation within the Armijo steps are not counted. 
nfctEval 
number of function evaluation. See value fctEval. 
fctEval 
vector of all function values calculated during the algorithm. It may be interesting if studying the convergence behaviour of the algorithm. Only if argument fctSave=TRUE 
gradient 
gradient of the objective function with respect to the fixedeffects coefficients 
maxGrad 
maximal value of the gradient which has to be close to zero for convergence. 
maxArmijo 
the maximal value of l used in each fixedeffects component. 
control 
see 
resid 
response residuals. See McCullagh and Nelder (1989). 
pearResid 
pearson residuals. See McCullagh and Nelder (1989). 
respResid 
response residuals. See McCullagh and Nelder (1989). 
workResid 
working residuals. See McCullagh and Nelder (1989). 
devResid 
deviance residuals. See McCullagh and Nelder (1989). 
Var 
conditional variance of the response at the current values. See McCullagh and Nelder (1989). 
di 
contribution of each observation to the deviance. See McCullagh and Nelder (1989). 
gof 
goodnessoffit criterion. For family="binomial", it is the insample missclassification rate. For family="poisson", it is the Pearson X2 statistic. See McCullagh and Nelder (1989). 
cpu 
cpu time needed for the algorithm. 
Juerg Schelldorfer, schell@stat.math.ethz.ch
P. McCullagh and J. A. Nelder (1989), Generalized Linear Models, ChapmanHall, London.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  ## (1) Use glmmlasso on the xerop data set
data(xerop)
fit1 < glmmlasso(y=xerop$y,group=xerop$group,X=xerop$X,Z=xerop$Z,
family="binomial",covStruct="Identity",lambda=30)
summary(fit1)
plot(fit1)
## (2) Use the glmmlasso on a small simulated data set
set.seed(142)
N < 40 ## number of groups
p < 6 ## number of covariates (including intercept)
q < 2 ## number of random effect covariates
ni < rep(10,N) ## observations per group
ntot < sum(ni) ## total number of observations
group < factor(rep(1:N,times=ni)) ## grouping variable
beta < c(0,1,1,1,1,0,0) # fixedeffects coefficients
X < cbind(1,matrix(rnorm(ntot*p),nrow=ntot)) ## fixedeffects design matrix
Z < t(glmer(rbinom(ntot,1,runif(ntot,0.3,1))~1+(1group)+(0+X2group),
data=data.frame(X),family="binomial")@Zt)
## randomeff. design matrix
bi < c(rnorm(N,0,2),rnorm(N,0,1))
eta < X%*%beta + Z%*%bi
mu < exp(eta)/(1+exp(eta))
y < rbinom(ntot,1,mu@x)
## random effects model with diagonal covariance matrix
fit2 < glmmlasso(y=y, group=group, X=X, Z=Z, family="binomial", lambda=10,
unpenalized=1:2, covStruct="Diagonal")
summary(fit2)
plot(fit2)

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