gbmokcv: Cross validation, n-fold for the hybrid method of generalized...

In spm: Spatial Predictive Modeling

Cross validation, n-fold for the hybrid method of generalized boosted regression modeling and ordinary kriging (gbmok)

Description

This function is a cross validation function for the hybrid method of generalized boosted regression modeling and ordinary kriging.

Usage

gbmokcv(
  longlat,
  trainx,
  trainy,
  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,
  nmax = 12,
  vgm.args = ("Sph"),
  block = 0,
  predacc = "VEcv",
  n.cores = 6,
  ...
)

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.
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.
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.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
n.cores	The number of CPU cores to use. See gbm for details. By default, 6 is used.
...	other arguments passed on to gbm.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv for categorical data: correct classification rate (ccr.cv) and kappa (kappa.cv)

Note

This function is largely based on rf.cv (see Li et al. 2013), rfcv in randomForest and gbm. When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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

Examples

## Not run: 
data(sponge)

gbmokcv1 <- gbmokcv(sponge[, c(1,2)], sponge[,-c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "ALL")
gbmokcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
gbmokcv1 <- gbmokcv(sponge[, c(1,2)], sponge[, -c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "VEcv")
VEcv [i] <- gbmokcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for gbmok", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

## End(Not run)

spm documentation built on May 6, 2022, 9:06 a.m.