Description Usage Arguments Value Author(s) References Examples

Perform variable selection and calculate posterior probabilities for the classes of linear and nonlinear of models.

1 2 | ```
BNNsel(X,Y,train_num,hid_num=3,lambda=0.025,total_iteration=1000000
,popN=20,nCPUs=20)
``` |

` X ` |
a |

` Y ` |
response vector. |

` train_num ` |
Number of training samples, should be smaller or equal to n. The default setting is the first 80% rows of the input samples. |

` hid_num ` |
Number of hidden units. The default setting is 3. |

` lambda` |
The prior probability for each connection of the neural network being selected for the final model. The default setting is 0.025. |

` total_iteration ` |
Number of iterations, recommend to be larger than or equal to 10,000. The default setting is 1000,000. |

` popN ` |
Number of Markov Chains in a parallel run, should be larger than or equal to 3. The default setting is 20. |

` nCPUs ` |
Number of CPUs to be used in the simulation. The default setting is 20. |

A list of five elements:

`net` |
Marginal inclusion probability of each connection of the neural network. |

`prob` |
Posterior probability of the class of linear models. |

`mar` |
Marginal inclusion probability of each input variable, which can be used for variable selection based on a multiple-hypothesis test or the median probability model criterion. |

`fit` |
Fitted value for the response vector of training data. |

`pred` |
Predicted value for the response vector of testing data. |

Bochao Jia and Faming Liang

Liang, F., Li, Q., and Zhou, L. (2017). Bayesian Neural Networks for Selection of Drug Sensitive Genes. Journal of the American Statistical Association.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## simulate data ##
library(BNN)
library(mvtnorm)
n <- 200
p <- 50
X <- rmvnorm(n, mean = rep(1,p), sigma = diag(rep(1,p)))
eps <- rnorm(n,0,0.5)
Y <- X[,1]*X[,2]+3*sin(X[,3])-2*cos(X[,4])+X[,5]^2+eps
### fit BNN model ###
BNNsel(X,Y,hid_num = 3,lambda=0.025,total_iteration = 100000, popN = 10,nCPUs = 10)
``` |

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