joinet (version 1.0.0)

joinet-package: 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.

Author

Maintainer: Armin Rauschenberger (ORCID)

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. tools:::Rd_expr_doi("10.1093/bioinformatics/btab576"). (Click here to access PDF.)

See Also

Useful links:

• https://github.com/rauschenberger/joinet

• https://rauschenberger.github.io/joinet/

• Report bugs at https://github.com/rauschenberger/joinet/issues

Examples

# \dontshow{
if(!grepl('SunOS',Sys.info()['sysname'])){
#--- 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
}# }
if (FALSE) {
#--- 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
}