rgpred: Generate spatial predictions using random forest in ranger...

In spm: Spatial Predictive Modeling

Generate spatial predictions using random forest in ranger (RG)

Description

This function is to make spatial predictions using random forest in ranger.

Usage

rgpred(
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else
    floor(sqrt(ncol(trainx))),
  num.trees = 500,
  min.node.size = NULL,
  type = "response",
  num.threads = NULL,
  verbose = FALSE,
  ...
)

Arguments

trainx	a dataframe or matrix contains columns of predictor 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.
mtry	Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
type	Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See ranger::predict.ranger for details.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
...	other arguments passed on to randomForest.

Value

A dataframe of longitude, latitude and predictions.

Note

This function is largely based on rfpred.

Author(s)

Jin Li

References

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)
data(petrel.grid)
set.seed(1234)
rgpred1 <- rgpred(petrel[, c(1,2, 6:9)], petrel[, 5], petrel.grid[, c(1,2)],
petrel.grid, num.trees = 500)
names(rgpred1)

## End(Not run)

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