# Predict Method for Geographical Random Forest Prediction of test data using the geographical random forest. ```r ## 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. ## Returns vector of predicted values ## 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 ## Author(s) Stamatis Kalogirou , Stefanos Georganos ## Note This function is under development. There should be improvements in future versions of the package `SpatialML`. Any suggestion is welcome! ## See Also `grf` ## Examples ```r ## 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) ```

predict.grf function