gbmidwpred | R Documentation |

This function is to make spatial predictions using the hybrid method of generalized boosted regression modeling and inverse distance weighting.

gbmidwpred( longlat, trainx, trainy, longlatpredx, predx, var.monotone = rep(0, ncol(trainx)), family = "gaussian", n.trees = 3000, learning.rate = 0.001, interaction.depth = 2, bag.fraction = 0.5, train.fraction = 1, n.minobsinnode = 10, cv.fold = 10, weights = rep(1, nrow(trainx)), keep.data = FALSE, verbose = TRUE, idp = 2, nmax = 12, n.cores = 6, ... )

`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. |

`var.monotone` |
an optional vector, the same length as the number of predictors, indicating which variables have a monotone increasing (+1), decreasing (-1), or arbitrary (0) relationship with the outcome. By default, a vector of 0 is used. |

`family` |
either a character string specifying the name of the distribution to use or a list with a component name specifying the distribution and any additional parameters needed. See gbm for details. By default, "gaussian" is used. |

`n.trees` |
the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. By default, 3000 is used. |

`learning.rate` |
a shrinkage parameter applied to each tree in the expansion. Also known as step-size reduction. |

`interaction.depth` |
the maximum depth of variable interactions. 1 implies an additive model, 2 implies a model with up to 2-way interactions, etc. By default, 2 is used. |

`bag.fraction` |
the fraction of the training set observations randomly selected to propose the next tree in the expansion. By default, 0.5 is used. |

`train.fraction` |
The first train.fraction * nrows(data) observations are used to fit the gbm and the remainder are used for computing out-of-sample estimates of the loss function. |

`n.minobsinnode` |
minimum number of observations in the trees terminal nodes. Note that this is the actual number of observations not the total weight. By default, 10 is used. |

`cv.fold` |
integer; number of folds in the cross-validation. it is also the number of cross-validation folds to perform within gbm. if > 1, then apply n-fold cross validation; the default is 10, i.e., 10-fold cross validation that is recommended. |

`weights` |
an optional vector of weights to be used in the fitting process. Must be positive but do not need to be normalized. If keep.data = FALSE in the initial call to gbm then it is the user's responsibility to resupply the weights to gbm.more. By default, a vector of 1 is used. |

`keep.data` |
a logical variable indicating whether to keep the data and an index of the data stored with the object. Keeping the data and index makes subsequent calls to gbm.more faster at the cost of storing an extra copy of the dataset. By default, 'FALSE' is used. |

`verbose` |
If TRUE, gbm will print out progress and performance indicators. By default, 'TRUE' is used. |

`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. |

`n.cores` |
The number of CPU cores to use. See gbm for details. By default, 6 is used. |

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

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse and vecv; or vecv for categorical data: correct classification rate (ccr.cv) and kappa (kappa.cv)

This function is largely based on gbm.

Jin Li

Li, J., J. Siwabessy, M. Tran, Z. Huang, and A. Heap. 2013. Predicting Seabed Hardness Using Random Forest in R. Pages 299-329 in Y. Zhao and Y. Cen, editors. Data Mining Applications with R. Elsevier.

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.

Greg Ridgeway with contributions from others (2015). gbm: Generalized Boosted Regression Models. R package version 2.1.1. https://CRAN.R-project.org/package=gbm

## Not run: data(petrel) data(petrel.grid) gbmidwpred1 <- gbmidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3], petrel.grid[, c(1,2)], petrel.grid, family = "gaussian", n.cores=6, nmax = 12) names(gbmidwpred1) ## End(Not run)

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