Usage Arguments Value See Also Examples

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1 |

`formula` |
a formula object for the model to be addressed |

`data` |
a data frame object containing variables and observations corresponding to the formula used |

`family` |
a string taking values of either "Gaussian" or "Bernoulli" correspodning to the linear or logistic Bayesian logic regression contexts |

`prior` |
character values "J" or "G" corresponing either to Jeffey's or robust g prior |

`report.level` |
a numeric value in (0,1) specifying the treshold for detections based on the marginal inclusion probabilities |

`d` |
population size for the GMJMCMC algorithm |

`cmax` |
the maximal allowed depth of logical expressions to be considered |

`kmax` |
the maximal number of logical expressions per model |

`p.and` |
probability of AND parameter of GMJMCMC algorithm |

`p.not` |
probability of applying logical NOT in GMJMCMC algorithm |

`p.surv` |
minimal survival probabilities for the features to be allowed to enter the next population |

`ncores` |
the maximal number of cores (and GMJMCMC threads) to be addressed in the analysis |

`n.mods` |
the number of the best models in the thread to calculate marginal inclusion probabilities |

`advanced` |
should only be adrresed by experienced users to tune advanced parameters of GMJMCMC, advanced corresponds to the vector of tuning parameters of runemjmcmc function |

a list of

`feat.stat` |
detected logical expressions and their marginal inclusion probabilities |

`predictions` |
NULL currently, since LogrRegr function is not designed for predictions at the moment, which is still possible in its expert mother function pinferunemjmcmc |

`allposteriors` |
all visited by GMJMCMC logical expressions and their marginal inclusion probabilities |

`threads.stats` |
a vector of detailed outputs of individual ncores threads of GMJMCMC run |

EMJMCMC::runemjmcmc, EMJMCMC::pinferunemjmcmc

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
set.seed(040590)
X1= as.data.frame(array(data = rbinom(n = 50*1000,size = 1,prob = runif(n = 50*1000,0,1)),dim = c(1000,50)))
Y1=rnorm(n = 1000,mean = 1+0.7*(X1$V1*X1$V4) + 0.8896846*(X1$V8*X1$V11)+1.434573*(X1$V5*X1$V9),sd = 1)
X1$Y1=Y1
#specify the initial formula
formula1 = as.formula(paste(colnames(X1)[51],"~ 1 +",paste0(colnames(X1)[-c(51)],collapse = "+")))
data.example = as.data.frame(X1)
#run the inference with robust g prior
res4G = LogicRegr(formula = formula1,data = data.example,family = "Gaussian",prior = "G",report.level = 0.5,d = 15,cmax = 2,kmax = 15,p.and = 0.9,p.not = 0.01,p.surv = 0.2,ncores = 32)
print(res4G$feat.stat)
#run the inference with Jeffrey's prior
res4J = LogicRegr(formula = formula1,data = data.example,family = "Gaussian",prior = "J",report.level = 0.5,d = 15,cmax = 2,kmax = 15,p.and = 0.9,p.not = 0.01,p.surv = 0.2,ncores = 32)
print(res4J$feat.stat)
``` |

aliaksah/EMJMCMC2016 documentation built on July 19, 2019, 12:01 p.m.

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