# lmmlasso: Function to fit high-dimensional (gaussian) linear... In lmmlasso: Linear mixed-effects models with Lasso

## Description

Fits the solution for a high-dimensional (gaussian) linear mixed-effects models

## Usage

 1 2 3 4 5 6 7 lmmlasso(x, ...) ## Default S3 method: lmmlasso(x, y, z = x, grp, weights = NULL,coefInit=NULL, lambda, startValue = 1, nonpen = 1:dim(z)[[2]], pdMat = c("pdIdent", "pdDiag","pdSym"), method = "ML", CovOpt = c("nlminb","optimize"), stopSat = TRUE, standardize = TRUE, control = lmmlassoControl(),ranInd=1:dim(z)[[2]],...) 

## Arguments

 x matrix of dimension ntot x p including the fixed-effects covariables. An intercept has to be included in the first column as (1,...,1). y response variable of length ntot. z random effects matrix of dimension ntot x q. It has to be a matrix, even if q=1. grp grouping variable of length ntot weights weights for the fixed-effects covariates: NA means no penalization, 0 means drop this covariate ; if given, the argument nonpen is ignored. By default each covariate has weight 1 coefInit list with three components used as starting values for the fixed effects, the random effects variance components and the error standard deviation. lambda positive regularization parameter startValue Choice of the starting values for the fixed effects using linear regression. 1 means 10-fold cross-validation with L1-penalty, 2 means 10-fold cross-validation Ridge Regression and 0 means that all the covariates are set to zero and the intercept is the mean of the response variable nonpen index of fixed effects with no penalization, ignored if the argument weights is specified, default is 1, which means that only the intercept (the first column in X )is not penalized. pdMat Covariance structure for the random effects. pdIdent, pdDiag and pdSym are already implemented. Default to pdIdent. pdIdent: b_i \sim \mathcal{N}(0,θ^2 I) (1 parameter), pdDiag: b_i \sim \mathcal{N}(0,diag(θ_1,…,θ_q)) (q parameters), pdSym: b_i \sim \mathcal{N}(0,Ψ) where Ψ is symmetric positive definit (q(q+1)/2 parameters) method Only "ML" is allowed. "REML" is not yet implemented. CovOpt which optimization routine should be used for updating the variance components parameters. optimize or nlminb. nlminb uses the estimate of the last iteration as a starting value. nlminb is faster if there are many Gauss-Seidel iterations. stopSat logical. Should the algorithm stop when ntot > p? standardize Should the x matrix be standardized such that each column has mean 0 and standard deviation 1? Be careful if the x matrix includes dummy variables. control control parameters for the algorithm and the Armijo Rule, see lmmlassoControl for the details ranInd Index of the random effects with respect to the x matrix ... not used.

## Details

All the details of the algorithm can be found in Schelldorfer et. al. (2010).

## Value

A lmmlasso object is returned, for which coef,resid, fitted, print, summary, plot methods exist.

 coefficients estimated fixed-effects coefficients \hat{β} pars free parameters in the covariance matrix Ψ of the random effects sigma standard deviation \hat{σ} of the errors random vector with random effects, sorted by groups u vector with the standardized random effects, sorted by effect ranef vector with random effects, sorted by effect fixef estimated fixed-effects coeffidients \hat{β} fitted.values The fitted values \hat{y} = \hat{X} β + Z \hat{b}_i residuals raw residuals y-\hat{y} Psi Covariance matrix Ψ of the random effects corPsi Correlation matrix of the random effects logLik value of the log-likelihood function deviance deviance=-2*logLik npar Number of parameters. Corresponds to the cardinality of the active set of coefficients plus the number of free parameters in Psi aic AIC bic BIC data data set, as a list with four components: x, y, z, grp (see above) weights weights for the fixed-effects covariates nonpen nonpenalized covariates. Differ from the input if weights is explicitely given coefInit list with three components used as starting values lambda positive regularization parameter converged Does the algorithm converge? 0: correct convergence ; an odd number means that maxIter was reached ; an even number means that the Armijo step was not succesful. For each unsuccessfull Armijo step, 2 is added to converged. If converged is large compared to the number of iterations counter, you may increase maxArmijo. counter The number of iterations used. stopped logical. Does the algorithm stopped due to ntot > p? pdMat Covariance structure for the random effects method "ML" CovOpt optimization routine control see lmmlassoControl call call stopped logical. Does the algorithm stopped due to a too large active set? ranInd Index of the random effects with respect to the x matrix objective Value of the objective function at the final estimates

## Author(s)

Juerg Schelldorfer, schell@stat.math.ethz.ch

## References

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

## Examples

  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 37 38 39 40 # (1) Use the lmmlasso on the classroomStudy data set data(classroomStudy) fit1 <- lmmlasso(x=classroomStudy$X,y=classroomStudy$y,z=classroomStudy$Z, grp=classroomStudy$grp,lambda=15,pdMat="pdIdent") summary(fit1) plot(fit1) # (2) Use the lmmlasso on a small simulated data set set.seed(54) N <- 20 # number of groups p <- 6 # number of covariates (including intercept) q <- 2 # number of random effect covariates ni <- rep(6,N) # observations per group ntot <- sum(ni) # total number of observations grp <- factor(rep(1:N,each=ni)) # grouping variable beta <- c(1,2,4,3,0,0,0) # fixed-effects coefficients x <- cbind(1,matrix(rnorm(ntot*p),nrow=ntot)) # design matrix bi1 <- rep(rnorm(N,0,3),each=ni) # Psi=diag(3,2) bi2 <- rep(rnorm(N,0,2),each=ni) bi <- rbind(bi1,bi2) z <- x[,1:2,drop=FALSE] y <- numeric(ntot) for (k in 1:ntot) y[k] <- x[k,]%*%beta + t(z[k,])%*%bi[,grp[k]] + rnorm(1) # correct random effects structure fit2 <- lmmlasso(x=x,y=y,z=z,grp=grp,lambda=10,pdMat="pdDiag") summary(fit2) plot(fit2) # wrong random effects structure fit3 <- lmmlasso(x=x,y=y,z=x[,1:3],grp=grp,lambda=10,pdMat="pdDiag") summary(fit3) plot(fit3) 

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