https://github.com/barbarabodinier/fake

R package fake (Flexible Data Simulation Using The Multivariate Normal Distribution).

https://github.com/barbarabodinier/fake

Repository

R package fake (Flexible Data Simulation Using The Multivariate Normal Distribution).

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# fake: Flexible Data Simulation Using The Multivariate Normal Distribution 


[![CRAN version](https://www.r-pkg.org/badges/version/fake)](https://cran.r-project.org/package=fake)
[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/last-month/fake?color=blue)](https://r-pkg.org/pkg/fake)
![GitHub last commit](https://img.shields.io/github/last-commit/barbarabodinier/fake?logo=GitHub&style=flat-square)
  

## Description

> This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures.


## Installation

The released version of the package can be installed from [CRAN](https://CRAN.R-project.org) with:

```{r installation cran, eval=FALSE}
install.packages("fake")
```

The development version can be installed from [GitHub](https://github.com/):

```{r installation github, eval=FALSE}
remotes::install_github("barbarabodinier/fake")
```

## Main functions

### Linear model

```{r linear regression, eval=FALSE}
library(fake)

set.seed(1)
simul <- SimulateRegression(n = 100, pk = 20)
head(simul$xdata)
head(simul$ydata)
```

### Logistic model

```{r logistic regression, eval=FALSE}
set.seed(1)
simul <- SimulateRegression(n = 100, pk = 20, family = "binomial")
head(simul$ydata)
```

### Structural causal model

```{r structural causal model, eval=FALSE}
set.seed(1)
simul <- SimulateStructural(n = 100, pk = c(3, 2, 3))
head(simul$data)
```

### Gaussian graphical model

```{r graphical modelling, eval=FALSE}
set.seed(1)
simul <- SimulateGraphical(n = 100, pk = 20)
head(simul$data)
```

### Gaussian mixture model

```{r clustering, eval=FALSE}
set.seed(1)
simul <- SimulateClustering(n = c(10, 10, 10), pk = 20)
head(simul$data)
```

## Extraction and visualisation of the results

The true model structure is returned in the output of any of the main functions in:

```{r theta, eval=FALSE}
simul$theta
```

The functions `print()`, `summary()` and `plot()` can be used on the outputs from the main functions.

## Reference

- Barbara Bodinier, Sarah Filippi, Therese Haugdahl Nost, Julien Chiquet and Marc Chadeau-Hyam. Automated calibration for stability selection in penalised regression and graphical models: a multi-OMICs network application exploring the molecular response to tobacco smoking. (2021) arXiv. [link](https://doi.org/10.48550/arXiv.2106.02521)

## Other resources

- R scripts to reproduce the simulation study (Bodinier et al. 2021) conducted using the functions in **fake** [link](https://github.com/barbarabodinier/stability_selection)

- R package **sharp** for stability selection and consensus clustering [link](https://github.com/barbarabodinier/sharp)

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