fNumP

Function for [Num]erical [P]redictions

Source

https://github.com/FLFgit/ScaleP/blob/master/functions/fNumP.R

Parameter

• RU.DIR -> directory containing reference unit (RU) file
• RU.SHP -> name of reference unit shape file
• SAMPLE.DIR -> directory containing sample data set
• SAMPLE.SHP -> name of sample data set
• PART -> proportion of training and test data set [0...1]
• OUT.DIR -> directory containing resulting files
• EPSG -> EPSG code of all data
• T.PM -> column name of target parameter in SAMPLE.SHP
• PM -> prefix of explaning attributes, which should be considered
• EXPORT=FALSE -> option: export of shape file with explaining parameters and prediction result

Result

• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_part[PART*100]_train.shp -> shape file of training data set
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_part[PART*100]_test.shp -> shape file of test data set
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_part[PART*100]_test.shp -> shape file of test data set
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_DP.pdf -> density plot of target parameters based on training and test data sets
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_VarImp.csv -> variable-specific variable importance
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_CV.csv -> accuracy metrics based on cross validation representing internal model performance
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_ACCtrain.csv -> accuracy metrics based on linear regression between predicted and observed parameters related to training data set
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_ACCtest.csv -> accuracy metrics based on linear regression between predicted and observed parameters related to test data set
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_SPtrain.pdf -> scatterplot of predicted and observed parameters related to training data set
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_SPtrain.pdf -> scatterplot of predicted and observed parameters related to test data set
• [RU.SHP]-[SAMPLE.SHP]_[T.PM]_MODEL-[M.TRAIN]_part[PART*100].shp -> optional: shape file with explaining parameters and prediction result (column [T.PM]_SIM)