rgidwcv {spm}

Cross validation, n-fold for the hybrid method of random forest in ranger and inverse distance weighting (RGIDW)

Description

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

Usage

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",
  ...
)

Arguments

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.

Value

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

Note

This function is largely based on rfidwcv.

Author(s)

Jin Li

References

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.

Examples

## 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)