rgidwpred | R Documentation |

This function is to make spatial predictions using the hybrid method of random forest in ranger and inverse distance weighting (RGIDW).

rgidwpred( longlat, trainx, trainy, longlatpredx, predx, mtry = function(p) max(1, floor(sqrt(p))), num.trees = 500, min.node.size = NULL, type = "response", num.threads = NULL, verbose = FALSE, idp = 2, nmax = 12, ... )

`longlat` |
a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy). |

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

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

`longlatpredx` |
a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted. |

`predx` |
a dataframe or matrix contains columns of predictive variables for the grids to be predicted. |

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

`num.trees` |
number of trees. By default, 500 is used. |

`min.node.size` |
Default 1 for classification, 5 for regression. |

`type` |
Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See ranger::predict.ranger for details. |

`num.threads` |
number of threads. Default is number of CPUs available. |

`verbose` |
Show computation status and estimated runtime.Default is FALSE. |

`idp` |
numeric; specify the inverse distance weighting power. |

`nmax` |
for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used. |

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

A dataframe of longitude, latitude and predictions.

This function is largely based on rfidwpred.

Jin Li

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

## Not run: data(petrel) data(petrel.grid) rgidwpred1 <- rgidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3], petrel.grid[, c(1,2)], petrel.grid, num.trees = 500, idp = 2, nmax = 12) names(rgidwpred1) ## End(Not run)

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