gbmpred | R Documentation |

This function is to make spatial predictions using generalized boosted regression modeling.

gbmpred( 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, n.cores = 6, ... )

`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 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. |

`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 dataframe of longitude, latitude and predictions.

This function is largely based on 'gbm'.

Jin Li

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(sponge) data(sponge.grid) gbmpred1 <- gbmpred(sponge[, -c(3)], sponge[, 3], sponge.grid[, c(1:2)], sponge.grid, family = "poisson", n.cores=2) names(gbmpred1) ## End(Not run)

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