spm2 (version 1.1.3)

rfkrigeidwcv: Cross validation, n-fold and leave-one-out for the hybrid methods of 'random forest' ('RF'), 'kriging' and inverse distance weighted ('IDW')

Description

This function is a cross validation function for 38 hybrid methods of 'RF', 'kriging' and 'IDW', including the average of 'rfkrige' and 'rfidw' ('rfkrigerfidw') and the average of 'rf', 'rfkrige' and 'rfidw' ('rfrfkrigerfidw'), where 'kriging' methods include ordinary kriging ('OK'), simple kriging ('SK'), block 'OK' ('BOK') and block 'SK'('BSK') and 'IDW' also covers 'NN' and 'KNN'.. The data splitting is based on a stratified random sampling method (see the 'datasplit' function for details).

Usage

rfkrigeidwcv(
  longlat,
  trainx,
  trainy,
  mtry = function(p) max(1, floor(sqrt(p))),
  ntree = 500,
  transformation = "none",
  delta = 1,
  formula = res1 ~ 1,
  vgm.args = c("Sph"),
  anis = c(0, 1),
  alpha = 0,
  block = 0,
  beta,
  nmaxkrige = 12,
  idp = 2,
  nmaxidw = 12,
  hybrid.parameter = 2,
  lambda = 1,
  validation = "CV",
  cv.fold = 10,
  predacc = "VEcv",
  ...
)

Value

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

Arguments

longlat

a dataframe contains longitude and latitude of point samples.

trainx

a dataframe or matrix contains columns of predictive variables.

trainy

a vector of the response variable.

mtry

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

ntree

number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. By default, 500 is used.

transformation

transform the residuals of 'rf' to normalize the data for 'krige'; can be "sqrt" for square root, "arcsine" for arcsine, "log" or "none" for non transformation. By default, "none" is used.

delta

numeric; to avoid log(0) in the log transformation. The default is 1.

formula

formula defining the response vector and (possible) regressor. an object (i.e., 'variogram.formula') for 'variogram' or a formula for 'krige'. see 'variogram' and 'krige' in 'gstat' for details.

vgm.args

arguments for 'vgm', e.g. variogram model of response variable and anisotropy parameters. see 'vgm' in 'gstat' for details. By default, "Sph" is used.

anis

anisotropy parameters: see notes 'vgm' in 'gstat' for details.

alpha

direction in plane (x,y). see variogram in 'gstat' for details.

block

block size. see 'krige' in 'gstat' for details.

beta

for simple kriging. see 'krige' in 'gstat' for details.

nmaxkrige

for a 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.

idp

a numeric number specifying the inverse distance weighting power.

nmaxidw

for a 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.

hybrid.parameter

the default is 2 that is for 'rfkrigerfidw'; for 'rfrfkrigerfidw', it needs to be 3.

lambda,

ranging from 0 to 2; the default is 1 for 'rfkrigerfidw' and 'rfrfkrigerfidw'; and if it is < 1, more weight is placed on 'krige', otherwise more weight is placed on 'idw'; and if it is 0, 'idw' is not considered and the resultant methods is 'rfkrige' when the default 'hybrid.parameter' is used; and if it is 2, then the resultant method is 'rfidw' when the default 'hybrid.parameter' is used.

validation

validation methods, include 'LOO': leave-one-out, and 'CV': cross-validation.

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.

predacc

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

...

other arguments passed on to 'randomForest', 'krige' and 'gstat'.

Author

Jin Li

References

Li, J. (2022). Spatial Predictive Modeling with R. Boca Raton, Chapman and Hall/CRC.

Li, J., Potter, A., Huang, Z., and Heap, A. (2012). Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods, Geoscience Australia, Record 2012/48, 115pp.

Li, J., Heap, A., Potter, A., and Danilel, J.J. (2011). Predicting Seabed Mud Content across the Australian Margin II: Performance of Machine Learning Methods and Their Combination with Ordinary Kriging and Inverse Distance Squared, Geoscience Australia, Record 2011/07, 69pp.

Liaw, A. and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

Examples

# \donttest{
library(spm)
# rfrfokrfidw
data(sponge)
longlat <- sponge[, 1:2]
set.seed(1234)
rfrfkrigerfidwcv1 <- rfkrigeidwcv(longlat = longlat,
trainx = sponge[, -3], trainy = sponge[, 3], formula = res1 ~ 1, vgm.args = ("Sph"),
nmaxkrige = 12, idp = 2, nmaxidw = 12, hybrid.parameter = 3, validation = "CV",
predacc = "ALL")
rfrfkrigerfidwcv1

# rfokrfidw for count data
data(sponge)
longlat <- sponge2[, 1:2]
y = sponge[, 3]
trainx = sponge[, -3]
set.seed(1234)
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
 rfkrigerfidwcv1 <- rfkrigeidwcv(longlat = longlat,
 trainx = trainx, trainy = y, formula = res1 ~ 1, vgm.args = ("Sph"),
 nmaxkrige = 12, idp = 2, nmaxidw = 12, validation = "CV",  predacc = "VEcv")
 VEcv [i] <- rfkrigerfidwcv1
 }
 plot(VEcv ~ c(1:n), xlab = "Iteration for rfokrfidw", ylab = "VEcv (%)")
 points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
 abline(h = mean(VEcv), col = 'blue', lwd = 2)
# }