predict.regAbcrf {abcrf} | R Documentation |

Based on a reg-ABC-RF object this function predicts the posterior expectation, median, variance, quantiles for the corresponding parameter given new dataset. Somes posterior errors can be computed at an higher computational price.

## S3 method for class 'regAbcrf' predict(object, obs, training, quantiles=c(0.025,0.975), paral = FALSE, ncores = if(paral) max(detectCores()-1,1) else 1, rf.weights = FALSE, post.err.med = FALSE, ...)

`object` |
a |

`obs` |
a data frame containing the summary statistics of the observed data sets. |

`training` |
the data frame containing the reference table used to train the |

`quantiles` |
numeric vector of probabilities with values in [0,1]. The default value is equal to |

`paral` |
a boolean that indicates if random forests predictions should be parallelized. |

`ncores` |
the number of CPU cores to use for the regression random forest predictions. If paral=TRUE, it is used the number of CPU cores minus 1. If ncores is not specified and |

`rf.weights` |
a boolean that indicates if the random forest weights used to predict quantities of interest should we returned. The default value is FALSE. |

`post.err.med` |
a boolean that indicates if posterior errors based on posterior medians should be computed. The default value is FALSE. If computed, this function might take a much more time. |

`...` |
optional arguments to be passed on to the function |

An object of class `regAbcrfpredict`

, which is a list with the following components:

`expectation` |
predicted posterior expectation for each oberved data set, |

`med` |
predicted posterior median for each oberved data set, |

`variance` |
predicted posterior variance for each observed data set, computed by reusing weights, this quantity is also the posterior mean squared error, |

`variance.cdf` |
predicted posterior variance for each observed data set, computed by approximation of the cumulative distribution function, |

`quantiles` |
predicted posterior quantiles for each observed data set, |

`weights` |
a matrix composed of the weights used to predict quantities of interest. Returned if |

`post.NMAE.mean` |
posterior normalized mean absolute error obtained using the out-of-bag posterior expectation (mean) and previously computed random forest weights, for each observed data set, |

`post.MSE.med` |
posterior mean squared error obtained using the out-of-bag posterior median and previously computed random forest weights, for each observed data set, |

`post.NMAE.med` |
posterior normalized mean absolute error obtained using the out-of-bag posterior expectation (mean) and previously computed random forest weights, for each observed data set, |

`prior.MSE` |
prior mean squared error computed with training out-of-bag prediction based on mean of response variable, |

`prior.NMAE` |
prior normalized mean absolute error computed with training out-of-bag predictions based on mean of response variable, |

`prior.MSE.med` |
prior mean squared error computed with training out-of-bag predictions based on median of response variable, |

`prior.NMAE.med` |
prior normalized mean absolute error with training out-of-bag predictions based on median of response variable, |

`prior.coverage` |
prior credible inteval coverage computed for training instances, if only two quantiles are of interest, NULL otherwise. |

Raynal L., Marin J.-M. Pudlo P., Ribatet M., Robert C. P. and Estoup, A. (2019)
*ABC random forests for Bayesian parameter inference* Bioinformatics
https://doi.org/10.1093/bioinformatics/bty867

`regAbcrf`

,
`predictOOB`

,
`plot.regAbcrf`

,
`err.regAbcrf`

,
`covRegAbcrf`

,
`ranger`

,
`densityPlot`

data(snp) modindex <- snp$modindex sumsta <- snp$sumsta[modindex == "3",] r <- snp$param$r[modindex == "3"] r <- r[1:500] sumsta <- sumsta[1:500,] data2 <- data.frame(r, sumsta) model.rf.r <- regAbcrf(r~., data2, ntree=100) data(snp.obs) predict(model.rf.r, snp.obs, data2)

[Package *abcrf* version 1.8.1 Index]