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.

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.

Armin Rauschenberger and Enrico Glaab (2019).
"joinet: predicting correlated outcomes jointly to improve clinical prognosis".
*Manuscript in preparation*.

#--- 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