avi | R Documentation |

This function is to derive an averaged variable importance based on random forest

avi( trainx, trainy, mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else floor(sqrt(ncol(trainx))), ntree = 500, importance = TRUE, maxk = c(4), nsim = 100, corr.threshold = 0.5, ... )

`trainx` |
a dataframe or matrix contains columns of predictor variables. |

`trainy` |
a vector of response, must have length equal to the number of rows in trainx. |

`mtry` |
a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call. |

`ntree` |
number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. By default, 500 is used. |

`importance` |
imprtance of predictive variables. |

`maxk` |
maxk split value. By default, 4 is used. |

`nsim` |
iteration number. By default, 100 is used. |

`corr.threshold` |
correlation threshold and the defaults value is 0.5. |

`...` |
other arguments passed on to randomForest. |

A list with the following components: averaged variable importance (avi), column number of importance variable in trainx arranged from the most important to the least important (impvar), names of importance variable arranged from the most important to the least important (impvar2)

Jin Li

Smith, S.J., Ellis, N., Pitcher, C.R., 2011. Conditional variable importance in R package extendedForest.

Li, J. 2013. Predicting the spatial distribution of seabed gravel content using random forest, spatial interpolation methods and their hybrid methods. Pages 394-400 The International Congress on Modelling and Simulation (MODSIM) 2013, Adelaide.

Liaw, A. and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

## Not run: data(petrel) set.seed(1234) avi1 <- avi(petrel[, c(1,2, 6:9)], petrel[, 5], nsim = 10) avi1 avi1 <- avi(petrel[, c(1), drop = FALSE], petrel[, 5], nsim = 10) avi1 ## End(Not run)

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