spfkm: Fit a supervised fuzzy kmeans model and predict memberships
In landmap: Automated Spatial Prediction using Ensemble Machine Learning

Description Usage Arguments Value Author(s) Examples

Runs supervised fuzzy k-means (Hengl et al., 2004; doi: 10.1080/13658810310001620924) using a list of covariates layers provided as "SpatialPixelsDataFrame-class" object. If class centres and variances are not provided, it first fits a multinomial logistic regression model (spmultinom), then predicts the class centers and variances based on the output from the nnet::multinom.

## S4 method for signature 
## 'formula,SpatialPointsDataFrame,SpatialPixelsDataFrame'
spfkm(
  formulaString,
  observations,
  covariates,
  class.c = NULL,
  class.sd = NULL,
  fuzzy.e = 1.2
)

`formulaString`	formula.
`observations`	SpatialPointsDataFrame.
`covariates`	SpatialPixelsDataFrame.
`class.c`	class centers (per variable).
`class.sd`	class standard deviation (per variable).
`fuzzy.e`	fuzzy coefficient.

A fuzzy kmeans model

Tom Hengl

if(requireNamespace("plotKML")){
library(plotKML)
library(sp)
library(nnet)
data(eberg)
# subset to 20%:
eberg <- eberg[runif(nrow(eberg))<.2,]
data(eberg_grid)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
# derive soil predictive components:
eberg_spc <- spc(eberg_grid, ~PRMGEO6+DEMSRT6+TWISRT6+TIRAST6)
# predict memberships:
formulaString = soiltype ~ PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10
eberg_sm <- spfkm(formulaString, eberg, eberg_spc@predicted)
# plot memberships:
  pal = seq(0, 1, 1/50)
  spplot(eberg_sm@mu, col.regions=grey(rev(pal)))
}