rfpred: Generate spatial predictions using random forest (RF)

In spm: Spatial Predictive Modeling

Generate spatial predictions using random forest (RF)

Description

This function is to make spatial predictions using random forest.

Usage

rfpred(
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else
    floor(sqrt(ncol(trainx))),
  ntree = 500,
  ...
)

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	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.
...	other arguments passed on to randomForest.

Value

A dataframe of longitude, latitude and predictions.

Author(s)

Jin Li

References

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

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rfpred1 <- rfpred(petrel[, c(1,2, 6:9)], petrel[, 5], petrel.grid[, c(1,2)],
petrel.grid, ntree = 500)
names(rfpred1)

## End(Not run)

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