It is really is worth the time to learn the code interface. Here is some annotated code for specifying a simple Random Forest image classification using spatial data.
# Add required libraries
require(randomForest)
require(sp)
require(rgdal)
require(raster)
# SET WORKING DIRECTORY
setwd("D:/ANALYSIS/Kenya_Hirola/RandomForest")
# Read point shapefile with training observations ("CLASS" field contains data)
sdata <- readOGR(getwd(), "L5_2010_02_Train")
# Read spectral data (ERDAS img image stack)
r <- stack( paste(getwd(), "L5_2010_02caldrkbdy.img", sep="/") )
# EXTRACT RASTER DATA TO POINTS
sdata@data <- data.frame(sdata@data, extract(r, sdata))
# CREATE RF MODEL
( rf.mdl <- randomForest(x=sdata@data[,3:ncol(sdata@data)], y=sdata@data[,"CLASS"],
ntree=501, proximity=TRUE, importance=TRUE) )
# PREDICT SINGLE CLASSIFIED RASTER
predict(r, rf.mdl, filename="ClassPred.img", type="response", na.rm=TRUE,
overwrite=FALSE, progress="window")