---
title: "S4 class structure of the xnet package"
author: "Meys Joris"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{xnet Class structure}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(xnet)

slotsToRmd <- function(class){
  slots <- getSlots(class)
  
  txt <- paste0(" * `",names(slots), "` : object of type **", slots, "**")
  cat(txt, sep = "\n")
}
```

This document describes the S4 class structure used in the `xnet` package.
It's mainly a reference for package developers. Users are advised to 
use the appropriate functions for extracting the information they need.

## Virtual classes

The `xnet` package has three virtual classes that each define a 
number of slots necessary for that specific type of model:

 1. the class `tskrr` for general two step kernel ridge regressions
 2. the class `tskrrTune` for tuned two step kernel ridge regressions
 3. the class `tskrrImpute` for two step kernel ridge regressions with
 imputed data.
 
Each of these classes defines the necessary slots for that specific 
type of action. The actual classes returned by the functions `tskrr()`,
`tune()` and `impute()` inherit from (a combination of) these virtual
classes.

## Actual classes

### Inheritance from tskrr

After using the function `tskrr()`, one of the following classes is
returned:

  * `tskrrHomogeneous` : for homogeneous networks
  * `tskrrHeterogeneous` : for heterogeneous networks
     
These classes have similar slots, but the homogeneous models don't need
information on the column kernel matrix. The slots are listed below. In
general the following design principles hold:

 * the slot `y` contains the adjacency matrix used to fit the model. This
 goes for all different classes, including the `tskrrTune` and `tskrrImpute` classes. 
 * the object stores the eigendecompositions of the row kernel K and
 -if applicable- the column kernel G. 
 * if `has.hat = TRUE`, the hat matrices Hk and possibly Hg are stored
 in the object as well. This can speed up calculations but comes at
 a memory cost.
 * the slot `pred` holds the predicted values.
 * the slots `lambda.k` and `lambda.g` store the tuning parameters.
 * the slot `labels` store the labels attached to the rows and the 
 columns in a list with elements `k` and `g`. If no labels were present,
 the single element `k` will have one `NA` value. The function `labels()`
 will still construct default labels when required.

### Slots defined by tskrrHomogeneous

```{r Create slots tskrrHomogeneous, results='asis', echo = FALSE}
slotsToRmd("tskrrHomogeneous")
```

### Slots defined by tskrrHeterogeneous

```{r Create slots tskrrHeterogeneous, results='asis', echo = FALSE}
slotsToRmd("tskrrHeterogeneous")
```

Both classes inherit directly from the class `tskrr`. But these 
classes also function as parent classes from which `tune()` related
and `impute()` related classes inherit.

### Inheritance from tskrrTune

When using the function `tune()`, you get one of the following classes:

  * `tskrrTuneHomogeneous` : for tuned homogeneous networks. Inherits also from `tskrrHomogeneous`.
  * `tskrrTuneHeterogeneous` : for tuned heterogeneous networks. Inherits also from `tskrrHeterogeneous`.
     
Apart from the slots of the respective `tskrr` class, the inheritance
from `tskrrTune` adds the following slots:

```{r Create slots tskrrTune, results='asis', echo = FALSE}
slotsToRmd("tskrrTune")
```

These slots use the following design principles:

 * `lambda_grid` is a list with one or two elements named `k` and `g`,
 similar to the `labels` slot of the `tskrr` classes. These elements
 contain the lambda values tested for that dimension. If the grid search
 was one-dimensional (and `onedim` contains `TRUE`), there's only one
 element called `k`. 
 * `loss_values` is always a matrix, but when `onedim = TRUE` it's a 
 matrix with a single column. In that matrix, the values are arranged in
 such a way that the rows correspond with the lambdas for the row kernel,
 and the columns with the lambdas for the column kernel.
 * The slots `exclusion` and `replaceby0` follow the same rules as the
 arguments of the function `get_loo_fun()`. 
     
### Inheritance from tskrrImpute

When using the function `impute()`, you get one of the following 
classes:

  * `tskrrImputeHomogeneous` : for homogeneous networks with imputed data.
  * `tskrrImputeHeterogeneous` : for heterogeneous networks with imputed data.

Apart from the slots of the respective `tskrr` class, the inheritance
from `tskrrImpute` adds the following slots:

```{r Create slots tskrrImpute, results='asis', echo = FALSE}
slotsToRmd("tskrrImpute")
```

The slot `imputeid` treats the Y matrix as a vector and stores the
position of the imputed values as a integer vector. The other two slots
just store the settings used during imputation.
