# splmm: Function to fit linear mixed-effects model with double... In splmm: Simultaneous Penalized Linear Mixed Effects Models

## Description

All the details of the algorithm can be found in the manuscript.

## Usage

 1 2 3 4 5 6 7 8 splmm(x, y, z, grp, lam1, lam2, nonpen.b=1,nonpen.L=1,penalty.b=c("lasso","scad"), penalty.L=c("lasso","scad"),CovOpt=c("nlminb","optimize"), standardize=TRUE,control=splmmControl()) ## Default S3 method: splmm(x, y, z, grp, lam1, lam2, nonpen.b=1,nonpen.L=1,penalty.b=c("lasso","scad"), penalty.L=c("lasso","scad"),CovOpt=c("nlminb","optimize"), standardize=TRUE,control=splmmControl()) 

## Arguments

 x matrix of dimension N 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 N. z random effects matrix of dimension N x q. It has to be a matrix, even if q=1. grp grouping variable of length N lam1 regularization parameter for fixed effects penalization. lam2 regularization parameter for random effects penalization. nonpen.b Index of indices of fixed effects not penalized. The default value is 1, which means the fixed intercept is not penalized nonpen.L Index of indices of random effects not penalized. The default value is 1, which means the random intercept is not penalized penalty.b The penalty method for fixed effects penalization. Currently available options include LASSO penalty and SCAD penalty. penalty.L The penalty method for fixed effects penalization. Currently available options include LASSO penalty and SCAD penalty. CovOpt which optimization routine should be used for updating the variance parameter. The available options include optimize and nlminb. nlminb uses the estimate of the last iteration as a starting value. nlminb is faster if there are many Gauss-Seidel iterations. standardize A logical parameter specifying whether the fixed effects matrix x and random effects matrix z should be standardized such that each column has mean 0 and standard deviation 1. The default value is TRUE control control parameters for the algorithm and the Armijo Rule, see 'splmmControl' for the details

## Value

A 'splmm' object is returned, for which coef,resid, fitted, print, summary methods exist.

 data data set used for fitting the model, as a list with four components: x, y, z, grp (see above) coefInit list of the starting values for beta, random effects covariance structure, and variance structure penalty.b The penalty method for fixed effects penalization. penalty.L The penalty method for random effects penalization. nonpen.b Index of indices of fixed effects not penalized. nonpen.L Index of indices of random effects not penalized. lambda1 regularization parameter for fixed effects penalization scaled by the number of subjects. lambda2 regularization parameter for random effects penalization the number of subjects. sigma standard deviation \hat{σ} of the errors D The estimates of the random effects covariance matrix \hat{D}. Lvec Vectorized \hat{L}, the lower triangular matrix of \hat{D} from Cholesky Decomposition. coefficients estimated fixed-effects coefficients \hat{β} random vector with random effects, sorted by groups ranef vector with random effects, sorted by effect u vector with the standardized 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} corD 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 set of nonzero coefficients plus the number of nonzero variance in D aic AIC bic BIC bicc Modified BIC defined by Wang et al (2009) ebic Extended BIC defined by Chen and Chen (2008) 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 indicating whether the algorithm stopped due to too many parameters, if yes need to increase lam1 or lam2 CovOpt optimization routine control see splmmControl objective Value of the objective function at the final estimates call call

## 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 ### Use splmm for a toy dataset. data(simulated_data) set.seed(144) fit = splmm(x=simulated_data$x,y=simulated_data$y, z=simulated_data$z,grp=simulated_data$grp, lam1=0.1,lam2=0.01, penalty.b="scad", penalty.L="scad") summary(fit) ## Use splmm on the Kenya school cognitive data set data(cognitive) x <- model.matrix(ravens ~schoolid+treatment+year+sex+age_at_time0 +height+weight+head_circ+ses+mom_read+mom_write +mom_edu, cognitive) z <- x fit <- splmm(x=x,y=cognitive$ravens,z=z,grp=cognitive$id,lam1=0.1, lam2=0.1,penalty.b="lasso", penalty.L="lasso") summary(fit) 

splmm documentation built on Sept. 8, 2021, 5:08 p.m.