I want to investigate how a vegetation type (4 categories) is explained by several environmental factors (height above sea level, disturbance magnitude, disturbance frequency, etc) - maybe some predictors could be excluded, maybe one predictor is the most relevant? Therefore I identified the vegetation type in the field in 160 locations (points, random sampling) and gathered the info of the environmental factors.
My idea is to build a random forest model (in R with the randomForest package) based on 70% of the points and then test the prediction accuracy against the other 30%. The importance of the predictors I want to evaluate by building different models (excluding some predictors) and then compare the accuracy of the model.
Would that be a appropriate way to do such an analyses?
Is it necessary to do the 70/30 split (i guess my model would get better if i use all points for training)?
In the next step I want to use the model to make a prediction for an area (cell wise) where I have raster maps of all environmental variables.