Description Usage Arguments Details Value References See Also Examples
Implements multivariate elastic net regression.
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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
|
nfolds |
number of folds |
foldid |
fold identifiers:
vector of length n with entries between 1 and |
type.measure |
loss function:
vector of length 1 or q with entries
|
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) |
weight |
input-output relations:
matrix with p rows (inputs) and q columns (outputs)
with entries 0 (exclude) and 1 (include),
or |
sign |
output-output relations:
matrix with q rows ("meta-inputs") and q columns (outputs),
with entries -1 (negative), 0 (none),
1 (positive) and NA (any),
or |
... |
further arguments passed to |
input-output relations:
In this matrix with p rows and q columns,
the entry in the jth row and the kth column
indicates whether the jth input may be used for
modelling the kth 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 lth row and the kth column indicates how the lth output may be used for modelling the kth 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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