Description Usage Arguments Details Value References See Also Examples

Implements multivariate elastic net regression.

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`Y` |
outputs |

`X` |
inputs |

`family` |
distribution |

`nfolds` |
number of folds |

`foldid` |
fold identifiers |

`type.measure` |
loss function |

`alpha.base` |
elastic net mixing parameter for base learners |

`alpha.meta` |
elastic net mixing parameter for meta learners |

`weight` |
input-output relations |

`sign` |
output-output relations |

`...` |
further arguments passed to |

**input-output relations:**
In this matrix with *p* rows and *q* columns,
the entry in the *j*th row and the *k*th column
indicates whether the *j*th input may be used for
modelling the *k*th output
(where *0* means "exclude" and
*1* means "include").
By default (`sign=NULL`

),
all entries are set to *1*.

**output-output relations:**
In this matrix with *q* rows and *q* columns,
the entry in the *l*th row and the *k*th column
indicates how the *l*th output may be used for
modelling the *k*th output
(where *-1* means negative effect,
*0* means no effect,
*1* means positive effect,
and *NA* means any effect).

There are three short-cuts for filling up this matrix:
(1) `sign=1`

sets all entries to *1* (non-negativity constraints).
This is useful if all pairs of outcomes
are assumed to be *positively* correlated
(potentially after changing the sign of some outcomes).
(2) `code=NA`

sets all diagonal entries to *1*
and all off-diagonal entries to `NA`

(no constraints).
(3) `sign=NULL`

uses Spearman correlation to determine the entries,
with *-1* for significant negative, *0* for insignificant,
*1* for significant positive correlations.

**elastic net:**
`alpha.base`

controls input-output effects,
`alpha.meta`

controls output-output effects;
lasso renders sparse models (`alpha`

*=1*),
ridge renders dense models (`alpha`

*=0*)

This function returns an object of class `joinet`

.
Available methods include
`predict`

,
`coef`

,
and `weights`

.
The slots `base`

and `meta`

each contain
*q* `cv.glmnet`

-like objects.

Armin Rauschenberger, Enrico Glaab (2021)
"Predicting correlated outcomes from molecular data"
*Bioinformatics*. btab576
doi: 10.1093/bioinformatics/btab576

`cv.joinet`

, vignette

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