Description Usage Arguments Details Value Note Author(s)

Function to simulate data *Y = X β + σ N(0, 1)*

1 2 |

`p` |
integer : number of variates. Should be >15 if |

`n` |
integer : number of observations |

`beta` |
vector with |

`C` |
matrix |

`r` |
scalar for calculating the covariance of X when |

`rSN` |
scalar : ratio signal/noise |

When `beta`

is `NULL`

, then `p`

should be
greater than 15 and
`beta=c(rep(2.5,5),rep(1.5,5),rep(0.5,5),rep(0,p-15))`

When `C`

is `NULL`

, then `C`

is block
diagonal with

`C[a,b] = r**abs(a-b)`

for *1 ≤ a, b ≤ 15*

`C[a,b] = r**abs(a-b)`

for *16 ≤ a, b ≤ p*

The lines of `X`

are `n`

i.i.d. gaussian variables with
mean 0 and covariance matrix `C`

.

The variance `sigma**2`

equals the squared euclidean
norm of *X β* divided by `rSN*n`

.

A list with components :

`Y` |
vector |

`X` |
matrix |

`C` |
matrix |

`sigma` |
scalar. See details. |

`beta` |
vector with |

Library `mvtnorm`

is loaded.

Yannick Baraud, Christophe Giraud, Sylvie Huet

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