The R package joinet implements multivariate
ridge and lasso regression using stacked generalisation.
This multivariate regression typically outperforms
univariate regression at predicting correlated outcomes.
It provides predictive and interpretable models
in high-dimensional settings.
Details
Use function joinet for model fitting.
Type library(joinet) and then ?joinet or
help("joinet)" to open its help file.
See the vignette for further examples.
Type vignette("joinet") or browseVignettes("joinet")
to open the vignette.
References
Armin Rauschenberger and Enrico Glaab (2021) "Predicting correlated outcomes from molecular data". Bioinformatics 37(21):3889–3895. doi:10.1093/bioinformatics/btab576 . (Click here to access PDF.)
Author
Maintainer: Armin Rauschenberger armin.rauschenberger@uni.lu (ORCID)
Examples
#>
#>
#>
#>
#>
if (FALSE) { # \dontrun{
#--- data simulation ---
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])))
# n samples, p inputs, q outputs
#--- model fitting ---
object <- joinet(Y=Y,X=X)
# slot "base": univariate
# slot "meta": multivariate
#--- make predictions ---
y_hat <- predict(object,newx=X)
# n x q matrix "base": univariate
# n x q matrix "meta": multivariate
#--- extract coefficients ---
coef <- coef(object)
# effects of inputs on outputs
# q vector "alpha": intercepts
# p x q matrix "beta": slopes
#--- model comparison ---
loss <- cv.joinet(Y=Y,X=X)
# cross-validated loss
# row "base": univariate
# row "meta": multivariate
} # }