cmenet (version 0.1.2)

cv.cmenet: Bi-level selection of conditional main effects

Description

The main function in this package. cv.cmenet performs variable selection of conditional main effects (CMEs) via a bi-level penalization framework, with penalty parameters tuned via cross-validation.

Usage

cv.cmenet(xme, xcme, y,
                    nfolds = 10, var.names = NULL,
                    nlambda.sib=20, nlambda.cou=20, lambda.min.ratio=1e-6,
                    ngamma=10, max.gamma=150, ntau=20,
                    max.tau=0.01, tau.min.ratio=0.01,
                    it.max=250, it.max.cv=25, warm.str="lasso")

Arguments

xme

An \(n\) x \(p\) binary model matrix for MEs.

xcme

An \(n\) x (4*choose(\(p\),2)) model matrix for CMEs.

y

An \(n\)-length response vector.

nfolds

Number of folds for cross-validation.

var.names

A (\(p\)+4*choose(\(p\),2))-length string vector for variable names.

nlambda.sib

Number of values for lambda.sib.

nlambda.cou

Number of values for lambda.cou.

lambda.min.ratio

Smallest value for lambda.sib and lambda.cou, as a fraction of lambda.max (the smallest penalty for which all coefficients are zero).

ngamma

Number of values for gamma.

max.gamma

Maximum value for gamma.

ntau

Number of values for tau.

max.tau

Maximum value for tau.

tau.min.ratio

Smallest value for tau, as a fraction of max.tau.

it.max

Number of optimization iterations.

it.max.cv

Number of optimization iterations in cross-validation.

warm.str

A string indicating which method should be used for warm-starting active variables. Current options include "lasso" (default) and "hierNet".

Value

cme.fit

Fitted cmenet object.

params

Fitted penalty parameters (lambda.sib, lambda.cou, gamma and tau).

select.names

Selected ME and CME variables.

select.idx

Indices for select.names.

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 {
  
# }
# 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)]

#---------------------------------------------------------------
# Selection of MEs and CMEs:
#---------------------------------------------------------------

## cmenet (parameters tuned via cross-validation)
cv.cme <- cv.cmenet(xme, xcme, y, var.names=colnames(model.mtx))
fit.cme <- cv.cme$cme.fit
sel.cme <- cv.cme$select.idx
colnames(model.mtx)[ind] #true model
colnames(model.mtx)[sel.cme] #selected effects from cmenet
colnames(model.mtx)[setdiff(sel.cme,ind)] #selected effects not in true model
colnames(model.mtx)[setdiff(ind,sel.cme)] #true effects not in selected model

## lasso
library(glmnet)
cv.las <- cv.glmnet(cbind(xme,xcme),y)
fit.las <- glmnet(cbind(xme,xcme),y)
sel.las <- which(fit.las$beta[,which(cv.las$lambda==cv.las$lambda.min)]!=0)
colnames(model.mtx)[ind] #true model
colnames(model.mtx)[sel.las] #selected effects from lasso
colnames(model.mtx)[setdiff(sel.las,ind)] #selected effects not in true model
colnames(model.mtx)[setdiff(ind,sel.las)] #true effects not in selected model

## sparsenet
library(sparsenet)
cv.sn <- cv.sparsenet(cbind(xme,xcme),y)
fit.sn <- sparsenet(cbind(xme,xcme),y)
sel.sn <- which(fit.sn$coefficients[[cv.sn$which.min[2]]]$beta[,cv.sn$which.min[1]]!=0)
colnames(model.mtx)[ind] #true model
colnames(model.mtx)[sel.sn] #selected effects from sparsenet
colnames(model.mtx)[setdiff(sel.sn,ind)] #selected effects not in true model
colnames(model.mtx)[setdiff(ind,sel.sn)] #true effects not in selected model

#---------------------------------------------------------------
## Comparison:
#---------------------------------------------------------------

## (a) Misspecifications
length(setdiff(sel.cme,ind)) + length(setdiff(ind,sel.cme)) # cmenet:    25
length(setdiff(sel.las,ind)) + length(setdiff(ind,sel.las)) # lasso:     29
length(setdiff(sel.sn,ind)) + length(setdiff(ind,sel.sn))   # sparsenet: 60

## (b) MSPE
set.seed(1000)
ntst <- 20
latmtx <- mvrnorm(ntst,p,mu=rep(0,p),Sigma=covmtx)
memtx <- (latmtx>=0)-(latmtx<0)
tst.mtx <- full.model.mtx(memtx)$model.mtx
xbtst <- inter + rowSums(tst.mtx[,ind])
ytst <- xbtst + rnorm(ntst,sd=1)

pred.cme <- predictcme(fit.cme,newx=tst.mtx)[,which(cv.cme$lambda.sib==cv.cme$params[1]),
            which(cv.cme$lambda.cou==cv.cme$params[2])]
pred.las <- predict(fit.las,newx=tst.mtx)[,which(cv.las$lambda==cv.las$lambda.min)]
pred.sn <- predict(fit.sn,newx=tst.mtx)[[which(fit.sn$gamma==cv.sn$parms.min[1])]][,
           which(fit.sn$lambda==cv.sn$parms.min[2])]
mean( (ytst-pred.cme)^2 ) # cmenet:    3.61
mean( (ytst-pred.las)^2 ) # lasso:     4.22
mean( (ytst-pred.sn)^2 )  # sparsenet: 4.00


  
# }