Compares univariate and multivariate regression.

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 (variables), with positive correlation (see details)

X

inputs: numeric matrix with \(n\) rows (samples) and \(p\) columns (variables)

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 learner: 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