| rgpred {spm} | R Documentation |
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)