MLmorph integratesmorphological modeling withmachine learning (random forests) to support structureddecision-making. The package enumerates amorphospace—the set of feasible configurations acrossselected variables—and estimates class probabilities over that space. Itincludes:
# install.packages("devtools")devtools::install_github("theogrost/MLmorph")library(MLmorph)set.seed(1)n<-120y<-factor(sample(letters[1:3], n,TRUE))x1<-factorize_numeric_vector(runif(n,10,20),breaks_no =3)x2<-factorize_numeric_vector(runif(n,1,2),breaks_no =4)df<-data.frame(y, x1, x2)# 1) Train a random forest and validate on a holdoutfit<-create_rf_model(df,dependent ="y",ntree =100,train_validate_split =0.75)fit$model# randomForest objecthead(fit$variables_importance)# importance per predictorfit$model_performance_on_test# caret::confusionMatrixms<-create_morphospace(df, fit$model)str(ms$morphospace)# Columns: predictors (x1, x2), '<dependent>' (class label), 'calculated' (probability),# 'purely_simulated' (TRUE if configuration not observed in df)# Example: top 10 configurations by predicted probabilityhead(ms$morphospace[order(ms$morphospace$calculated,decreasing =TRUE), ],10)# Probability mass per class (sums to 1 for each unique predictor combination)aggregate(calculated~ x1+ x2,data = ms$morphospace, sum)# Numeric → ordered factor via equal-frequency bins (default)z<-runif(12)fz<-factorize_numeric_vector(z,breaks_no =3)# Logical → factor with labeled levelsfb<-factorize_binary_vector(c(TRUE,FALSE,TRUE))# Character → factor with stable, numbered labelsfc<-factorize_character_vector(c("alpha","beta","alpha"))# Apply heuristics column-wisedf2<-factorize_nicely_dataframe(data.frame(a =runif(20),b =c(TRUE,FALSE),c =c("x","y","x","z")))str(df2)# CSVtmp_csv<-tempfile(fileext =".csv")utils::write.csv(data.frame(a =1:3,b =c("x","y","z")), tmp_csv,row.names =FALSE)dat_csv<-load_data(tmp_csv)# JSONtmp_json<-tempfile(fileext =".json")jsonlite::write_json(list(a =1:2,b =c("u","v")), tmp_json,auto_unbox =TRUE)dat_json<-load_data(tmp_json)# XLSXtmp_xlsx<-tempfile(fileext =".xlsx")openxlsx::write.xlsx(data.frame(a =1:2,b =c("m","n")), tmp_xlsx)dat_xlsx<-load_data(tmp_xlsx)# Launch the interactive app from the installed package# (opens in browser; not run during automated checks)# MLmorph()To ensure reproducibility, record: - the variable set andfactorization (binning method and parameters), - model hyperparameters(e.g.,ntree), - the random seed and train/validatesplit.
If you use MLmorph in research or consulting reports, please cite thepackage and your model specification.
MIT © Oskar Kosch. SeeLICENSE.md for details.
Please report bugs or feature requests at this repository’s Issuespage.