README.Rmd

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output: github_document
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knitr::opts_chunk$set(
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# RaJIVE

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RaJIVE (Robust Angle based Joint and Individual Variation Explained) is a robust alternative to the aJIVE method for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust with respect to outliers and other types of noises present in the data.

## Installation

You can install the released version of RaJIVE from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("RaJIVE")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("ericaponzi/RaJIVE")
```
## Example

This is a basic example which shows how to use RaJIVE on simple simulated data: 


### Running robust aJIVE


```{r example}
library(RaJIVE)
## basic example code
n <- 50
pks <- c(100, 80, 50)
Y <- ajive.data.sim(K =3, rankJ = 3, rankA = c(7, 6, 4), n = n,
                   pks = pks, dist.type = 1)

initial_signal_ranks <-  c(7, 6, 4)
data.ajive <- list((Y$sim_data[[1]]), (Y$sim_data[[2]]), (Y$sim_data[[3]]))
ajive.results.robust <- Rajive(data.ajive, initial_signal_ranks)

```


The function returns a list containing the aJIVE decomposition, with the joint component (shared across data sources),  individual component (data source specific) and residual component for each data source. 



### Visualizing results: 

- Joint rank: 

```{r ranks}

get_joint_rank(ajive.results.robust)

```

- Individual ranks: 

```{r}
get_individual_rank(ajive.results.robust, 1)
get_individual_rank(ajive.results.robust, 2)
get_individual_rank(ajive.results.robust, 3)
```

- Heatmap decomposition:

```{r}
decomposition_heatmaps_robustH(data.ajive, ajive.results.robust)
```

- Proportion of variance explained:

```{r}
showVarExplained_robust(ajive.results.robust, data.ajive)
```