# joinet-package: Multivariate Elastic Net Regression In joinet: Multivariate Elastic Net Regression

## Description

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```## Not run: #--- 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 ## End(Not run) ```

joinet documentation built on Aug. 9, 2021, 9:13 a.m.