bits {bravo} | R Documentation |

## Bayesian Iterated Screening (ultra-high, high or low dimensional).

### Description

Perform Bayesian iterated screening in Gaussian regression models

### Usage

```
bits(X, y, lam = 1, w = 0.5, pp = FALSE, max.var = nrow(X), verbose = TRUE)
```

### Arguments

`X` |
An |

`y` |
The response vector of length |

`lam` |
The slab precision parameter. Default: |

`w` |
The prior inclusion probability of each variable. Default: |

`pp` |
Boolean: If |

`max.var` |
The maximum number of variables to be included. |

`verbose` |
If |

### Value

A list with components

`model.pp` |
An integer vector of the screened model. |

`postprobs` |
The sequence of posterior probabilities until the last included variable. |

`lam` |
The value of lam, the slab precision parameter. |

`w` |
The value of w, the prior inclusion probability. |

### References

Wang, R., Dutta, S., Roy, V. (2021) Bayesian iterative screening in ultra-high dimensional settings. https://arxiv.org/abs/2107.10175

### Examples

```
n=50; p=100;
TrueBeta <- c(rep(5,3),rep(0,p-3))
rho <- 0.6
x1 <- matrix(rnorm(n*p), n, p)
X <- sqrt(1-rho)*x1 + sqrt(rho)*rnorm(n)
y <- 0.5 + X %*% TrueBeta + rnorm(n)
res<-bits(X,y, pp=TRUE)
res$model.pp # the vector of screened model
res$postprobs # the log (unnormalized) posterior probabilities corresponding to the model.pp.
```

*bravo*version 3.2.1 Index]