rgidwcv | R Documentation |

This function is a cross validation function for the hybrid method of random forest in ranger and inverse distance weighting (RGIDW).

rgidwcv( longlat, trainx, trainy, cv.fold = 10, mtry = function(p) max(1, floor(sqrt(p))), num.trees = 500, min.node.size = NULL, num.threads = NULL, verbose = FALSE, idp = 2, nmax = 12, predacc = "VEcv", ... )

`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. |

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

`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. |

`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. |

`predacc` |
can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc. |

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

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv.

This function is largely based on rfidwcv.

Jin Li

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.

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) rgidwcv1 <- rgidwcv(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 5], predacc = "ALL") rgidwcv1 n <- 20 # number of iterations, 60 to 100 is recommended. VEcv <- NULL for (i in 1:n) { rgidwcv1 <- rgidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5], predacc = "VEcv") VEcv [i] <- rgidwcv1 } plot(VEcv ~ c(1:n), xlab = "Iteration for RFIDW", ylab = "VEcv (%)") points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2) abline(h = mean(VEcv), col = 'blue', lwd = 2) n <- 20 # number of iterations, 60 to 100 is recommended. measures <- NULL for (i in 1:n) { rgidwcv1 <- rgidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5], predacc = "ALL") measures <- rbind(measures, rgidwcv1$vecv) } plot(measures ~ c(1:n), xlab = "Iteration for RFIDW", ylab = "VEcv (%)") points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2) abline(h = mean(measures), col = 'blue', lwd = 2) ## End(Not run)

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