Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or in the case of Random Forest Models, with Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
|Author||Elizabeth Freeman, Tracey Frescino|
|Date of publication||2016-07-03 10:53:52|
|Maintainer||Elizabeth Freeman <email@example.com>|
build.rastLUT: Build a raster Look-UP-Table for training dataset
col2trans: colors to transparent colors
get.test: Randomly Divide Data into Training and Test Sets
internal: Internal ModelMap Functions
model.build: Model Building
model.diagnostics: Model Predictions and Diagnostics
model.explore: Exploratory data analysis
model.importance.plot: Compares the variable importance of two models with a back to...
model.interaction.plot: plot of two-way model interactions
model.mapmake: Map Making
ModelMap-package: Modeling and Map Production using Random Forest and...