cmenet {cmenet} | R Documentation |
Bi-level selection of conditional main effects (fixed parameters)
Description
cmenet
performs variable selection of conditional main effects (CMEs) via a bi-level penalization framework, given fixed penalty parameters.
Usage
cmenet(xme, xcme, y,
lambda.sib=exp(seq(from=log(max.lambda),to=log(max.lambda*1e-6),length=20)),
lambda.cou=exp(seq(from=log(max.lambda),to=log(max.lambda*1e-6),length=20)),
max.lambda=lambda0.cme(cbind(xme,xcme),y),
gamma=1/(0.5-tau)+0.001, tau=0.01,
act.vec=rep(1,ncol(xme)+ncol(xcme)),
beta0=rep(0,ncol(xme)+ncol(xcme)),
it.max=250, lambda.flg=T)
Arguments
xme |
An |
xcme |
An |
y |
An |
lambda.sib |
Penalty vector for sibling CMEs. |
lambda.cou |
Penalty vector for cousin CMEs. |
max.lambda |
Maximum penalty value. |
gamma |
Bridge parameter in MC+ penalty. |
tau |
Coupling parameter for CMEs. |
act.vec |
A ( |
beta0 |
Initial regression coefficients. |
it.max |
Number of optimization iterations. |
lambda.flg |
Use the default option TRUE (unless within |
Value
coefficients |
Array of regression coefficients (over different |
residuals |
Array of regression residuals (over different |
inter |
Matrix of intercept estimates (over different |
References
Mak and Wu (2018). cmenet: a new method for bi-level variable selection of conditional main effects. Journal of the American Statistical Association, to appear.
Examples
## Not run:
library(MASS)
n <- 50 #number of observations
p <- 50 #number of main effects
## Simulate model matrix for MEs and CMEs
set.seed(1)
rho <- 0 #correlation
ones <- matrix(1,p,p)
covmtx <- rho*ones+(1-rho)*diag(p)
latmtx <- mvrnorm(n,p,mu=rep(0,p),Sigma=covmtx) #equicorrelated cov. matrix
memtx <- (latmtx>=0)-(latmtx<0) #simulate model matrix for MEs
model.mtx <- full.model.mtx(memtx)$model.mtx #generate model matrix for MEs and CMEs
## Set true model and generate response
num.act <- 2 # two siblings active
num.grp <- 4 # ... within four active groups
ind <- c()
for (ii in 1:num.grp){
eff <- sample(seq(2*(p-1)),num.act)
ind <- c(ind, p + eff + (ii-1)*(2*(p-1)))
}
colnames(model.mtx)[ind] # active CMEs
des.mtx <- model.mtx[,ind]
inter <- 12 #intercept
xbtrue <- inter + rowSums(des.mtx)
y <- xbtrue + rnorm(n,sd=1) #response
xme <- model.mtx[,1:p]
xcme <- model.mtx[,(p+1):ncol(model.mtx)]
## Run cmenet
cv.cme <- cv.cmenet(xme, xcme, y, var.names=colnames(model.mtx))
## End(Not run)