predict.grf: Predict Method for Geographical Random Forest

In SpatialML: Spatial Machine Learning

Predict Method for Geographical Random Forest

Description

Prediction of test data using the geographical random forest.

Usage

## S3 method for class 'grf'
predict(object, new.data, x.var.name, y.var.name, local.w=1, global.w=0,...)

Arguments

object	an object that created by the function grf that includes all local forests.
new.data	a data frame containing new data.
x.var.name	the name of the variable with X coordinates.
y.var.name	the name of the variable with Y coordinates.
local.w	weight of the local model predictor allowing semi-local predictions. Default value is 1.
global.w	weight of the global model predictor allowing semi-local predictions. Default value is 0.
...	for other arguments passed to the generic predict functions. For example you may pass here the number of threats

Details

A Geographical Random Forest prediction on unknown data. The nearest local random forest model in coordinate space is used to predict in each unknown y-variable location.

Value

vector of predicted values

Note

This function is under development. There should be improvements in future versions of the package SpatialML. Any suggestion is welcome!

Author(s)

Stamatis Kalogirou <[e-mail]>, Stefanos Georganos <[e-mail]>

References

Stefanos Georganos, Tais Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine Vanhuysse, Nicholus Odhiambo Mboga, Eléonore Wolff & Stamatis Kalogirou (2019) Geographical Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Heterogeneity in Remote Sensing and Population Modelling, Geocarto International, DOI: 10.1080/10106049.2019.1595177

See Also

grf

Examples

  ## Not run: 
      RDF <- random.test.data(10,10,3)
      Coords<-RDF[ ,4:5]
      grf <- grf(dep ~ X1 + X2, dframe=RDF, bw=10,
                kernel="adaptive", coords=Coords)

      RDF.Test <- random.test.data(2,2,3)

      predict.grf(grf, RDF.Test, x.var.name="X", y.var.name="Y", local.w=1, global.w=0)
  
## End(Not run)
  
      #Load the sample data
      data(Income)

      #Create the vector of XY coordinates
      Coords<-Income[,1:2]

      #Fit local model
      grf <- grf(Income01 ~ UnemrT01 + PrSect01, dframe=Income, bw=60,
                kernel="adaptive", coords=Coords)

      #Create New Random Data - XY coordinates inside the sample data map extend
      x<-runif(20, min = 142498, max = 1001578)
      y<-runif(20, min = 3855768, max = 4606754)
      u<-runif(20, min = 5, max = 50)
      p<-runif(20, min = 0, max = 100)
      f<-runif(20, min = 2, max = 30)
      df2<-data.frame(X=x, Y= y,  UnemrT01=u,  PrSect01=p,  Foreig01=f)

      #Make predictions using the local model
      predict.grf(grf, df2, x.var.name="X", y.var.name="Y", local.w=1, global.w=0)
  

SpatialML documentation built on Nov. 8, 2023, 1:08 a.m.