# predictcme: Predict using a fitted 'cmenet' object In cmenet: Bi-Level Selection of Conditional Main Effects

 predictcme R Documentation

## Predict using a fitted `cmenet` object

### Description

`predictcme` performs prediction at new ME settings `newx`, given fitted `cmenet` object.

### Usage

```  predictcme(fit.cme,newx)
```

### Arguments

 `fit.cme` Fitted object from `cmenet`. `newx` An m x p binary matrix for prediction (m new ME settings, p binary MEs).

### Examples

```  ## Not run:

library(MASS)
library(cmenet)
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 cv.cmenet
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

## Prediction
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])]

## End(Not run)
```

cmenet documentation built on May 28, 2022, 1:06 a.m.