---
title: Multivariate Elastic Net Regression
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{vignette}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=TRUE,eval=!grepl('SunOS',Sys.info()['sysname']))
set.seed(1)
```

## Installation

Install the current release from [CRAN](https://CRAN.R-project.org/package=joinet):

```{r,eval=FALSE}
install.packages("joinet")
```

Or install the latest development version from [GitHub](https://github.com/rauschenberger/joinet):

```{r,eval=FALSE}
#install.packages("devtools")
devtools::install_github("rauschenberger/joinet")
```

## Initialisation

Load and attach the package:

```{r}
library(joinet)
```

And access the [documentation](https://rauschenberger.github.io/joinet/):

```{r,eval=FALSE}
?joinet
help(joinet)
browseVignettes("joinet")
```

## Simulation

For `n` samples, we simulate `p` inputs (features, covariates) and `q` outputs (outcomes, responses). We assume high-dimensional inputs (`p` $\gg$ `n`) and low-dimensional outputs (`q` $\ll$ `n`).

```{r}
n <- 100
q <- 2
p <- 500
```

We simulate the `p` inputs from a multivariate normal distribution. For the mean, we use the `p`-dimensional vector `mu`, where all elements equal zero. For the covariance, we use the `p` $\times$ `p` matrix `Sigma`, where the entry in row $i$ and column $j$ equals `rho`$^{|i-j|}$. The parameter `rho`  determines the strength of the correlation among the inputs, with small `rho` leading weak correlations, and large `rho` leading to strong correlations (0 < `rho` < 1). The input matrix `X` has `n` rows and `p` columns.

```{r}
mu <- rep(0,times=p)
rho <- 0.90
Sigma <- rho^abs(col(diag(p))-row(diag(p)))
X <- MASS::mvrnorm(n=n,mu=mu,Sigma=Sigma)
```

We simulate the input-output effects from independent Bernoulli distributions. The parameter `pi` determines the number of effects, with small `pi` leading to few effects, and large `pi` leading to many effects (0 < `pi` < 1). The scalar `alpha` represents the intercept, and the `p`-dimensional vector `beta` represents the slopes.

```{r}
pi <- 0.01
alpha <- 0
beta <- rbinom(n=p,size=1,prob=pi)
```

From the intercept `alpha`, the slopes `beta` and the inputs `X`, we calculate the linear predictor, the `n`-dimensional vector `eta`. Rescale the linear predictor to make the effects weaker or stronger. Set the argument `family` to `"gaussian"`, `"binomial"`, or `"poisson"` to define the distribution. The `n` times `p` matrix `Y` represents the outputs. We assume the outcomes are *positively* correlated.

```{r,results="hide"}
eta <- alpha + X %*% beta
eta <- 1.5*scale(eta)
family <- "gaussian"

if(family=="gaussian"){
  mean <- eta
  Y <- replicate(n=q,expr=rnorm(n=n,mean=mean))
}

if(family=="binomial"){
  prob <- 1/(1+exp(-eta))
  Y <- replicate(n=q,expr=rbinom(n=n,size=1,prob=prob))
}

if(family=="poisson"){
  lambda <- exp(eta)
  Y <- replicate(n=q,expr=rpois(n=n,lambda=lambda))
}

cor(Y)
```

## Application

The function `joinet` fits univariate and multivariate regression. Set the argument `alpha.base` to 0 (ridge) or 1 (lasso).

```{r}
object <- joinet(Y=Y,X=X,family=family)
```

Standard methods are available. The function `predict` returns the predicted values from the univariate (`base`) and multivariate (`meta`) models. The function `coef` returns the estimated intercepts (`alpha`) and slopes (`beta`) from the multivariate model (input-output effects). And the function `weights` returns the weights from stacking (output-output effects).

```{r,eval=FALSE}
predict(object,newx=X)

coef(object)

weights(object)
```

The function `cv.joinet` compares the predictive performance of univariate (`base`) and multivariate (`meta`) regression by nested cross-validation. The argument `type.measure` determines the loss function.

```{r}
cv.joinet(Y=Y,X=X,family=family)
```

## Reference

Armin Rauschenberger
[![AR](https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png)](https://orcid.org/0000-0001-6498-4801)
and Enrico Glaab
[![EG](https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png)](https://orcid.org/0000-0003-3977-7469)
(2021).
"Predicting correlated outcomes from molecular data”. *Bioinformatics*
37(21):3889–3895.
[doi: 10.1093/bioinformatics/btab576](https://doi.org/10.1093/bioinformatics/btab576).
(Click [here](https://orbilu.uni.lu/bitstream/10993/47788/1/joinet.pdf) to access PDF.)
