cv.joinet: Model comparison

In joinet: Multivariate Elastic Net Regression

Description

Compares univariate and multivariate regression.

Usage

cv.joinet(
  Y,
  X,
  family = "gaussian",
  nfolds.ext = 5,
  nfolds.int = 10,
  foldid.ext = NULL,
  foldid.int = NULL,
  type.measure = "deviance",
  alpha.base = 1,
  alpha.meta = 1,
  compare = FALSE,
  mice = FALSE,
  cvpred = FALSE,
  times = FALSE,
  ...
)

Arguments

Y	outputs: numeric matrix with n rows (samples) and q columns (outputs)
X	inputs: numeric matrix with n rows (samples) and p columns (inputs)
family	distribution: vector of length 1 or q with entries "gaussian", "binomial" or "poisson"
nfolds.ext	number of external folds
nfolds.int	number of internal folds
foldid.ext	external fold identifiers: vector of length n with entries between 1 and nfolds.ext; or NULL
foldid.int	internal fold identifiers: vector of length n with entries between 1 and nfolds.int; or NULL
type.measure	loss function: vector of length 1 or q with entries "deviance", "class", "mse" or "mae" (see cv.glmnet)
alpha.base	elastic net mixing parameter for base learners: numeric between 0 (ridge) and 1 (lasso)
alpha.meta	elastic net mixing parameter for meta learners: numeric between 0 (ridge) and 1 (lasso)
compare	experimental arguments: character vector with entries "mnorm", "spls", "mrce", "sier", "mtps", "rmtl", "gpm" and others (requires packages spls, MRCE, SiER, MTPS, RMTL or GPM)
mice	missing data imputation: logical (mice=TRUE requires package mice)
cvpred	return cross-validated predictions: logical
times	measure computation time: logical
...	further arguments passed to glmnet and cv.glmnet

Value

This function returns a matrix with q columns, including the cross-validated loss from the univariate models (base), the multivariate models (meta), and the intercept-only models (none).

Examples

## Not run: 
n <- 50; p <- 100; q <- 3
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
cv.joinet(Y=Y,X=X)
## End(Not run)

## Not run: 
# correlated features
n <- 50; p <- 100; q <- 3
mu <- rep(0,times=p)
Sigma <- 0.90^abs(col(diag(p))-row(diag(p)))
X <- MASS::mvrnorm(n=n,mu=mu,Sigma=Sigma)
mu <- rowSums(X[,sample(seq_len(p),size=5)])
Y <- replicate(n=q,expr=rnorm(n=n,mean=mu))
#Y <- t(MASS::mvrnorm(n=q,mu=mu,Sigma=diag(n)))
cv.joinet(Y=Y,X=X)
## End(Not run)

## Not run: 
# other distributions
n <- 50; p <- 100; q <- 3
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
eta <- rowSums(X[,1:5])
Y <- replicate(n=q,expr=rbinom(n=n,size=1,prob=1/(1+exp(-eta))))
cv.joinet(Y=Y,X=X,family="binomial")
Y <- replicate(n=q,expr=rpois(n=n,lambda=exp(scale(eta))))
cv.joinet(Y=Y,X=X,family="poisson")
## End(Not run)

## Not run: 
# uncorrelated outcomes
n <- 50; p <- 100; q <- 3
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
y <- rnorm(n=n,mean=rowSums(X[,1:5]))
Y <- cbind(y,matrix(rnorm(n*(q-1)),nrow=n,ncol=q-1))
cv.joinet(Y=Y,X=X)
## End(Not run)

## Not run: 
# sparse and dense models
n <- 50; p <- 100; q <- 3
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
set.seed(1) # fix folds
cv.joinet(Y=Y,X=X,alpha.base=1) # lasso
set.seed(1)
cv.joinet(Y=Y,X=X,alpha.base=0) # ridge
## End(Not run)

joinet documentation built on Aug. 9, 2021, 9:13 a.m.