Function reference • radiant.model

Model > Linear regression (OLS) Estimate linear regression models
`regress()`	Linear regression using OLS
`summary(<regress>)`	Summary method for the regress function
`predict(<regress>)`	Predict method for the regress function
`print(<regress.predict>)`	Print method for predict.regress
`plot(<regress>)`	Plot method for the regress function
Model > Logistic regression Estimate logistic regression models
`logistic()`	Logistic regression
`summary(<logistic>)`	Summary method for the logistic function
`predict(<logistic>)`	Predict method for the logistic function
`print(<logistic.predict>)`	Print method for logistic.predict
`plot(<logistic>)`	Plot method for the logistic function
`confint_robust()`	Confidence interval for robust estimators
Model > Multinomial logistic regression Estimate multinomial logistic regression models
`mnl()`	Multinomial logistic regression
`summary(<mnl>)`	Summary method for the mnl function
`predict(<mnl>)`	Predict method for the mnl function
`print(<mnl.predict>)`	Print method for mnl.predict
`plot(<mnl.predict>)`	Plot method for mnl.predict function
`store(<mnl.predict>)`	Store predicted values generated in the mnl function
`plot(<mnl>)`	Plot method for the mnl function
Model > Neural network Estimate neural network models
`nn()`	Neural Networks using nnet
`summary(<nn>)`	Summary method for the nn function
`predict(<nn>)`	Predict method for the nn function
`print(<nn.predict>)`	Print method for predict.nn
`plot(<nn>)`	Plot method for the nn function
`cv.nn()`	Cross-validation for a Neural Network
Model > Naive Bayes Estimate naive Bayes models
`nb()`	Naive Bayes using e1071::naiveBayes
`summary(<nb>)`	Summary method for the nb function
`predict(<nb>)`	Predict method for the nb function
`print(<nb.predict>)`	Print method for predict.nb
`plot(<nb.predict>)`	Plot method for nb.predict function
`store(<nb.predict>)`	Store predicted values generated in the nb function
`plot(<nb>)`	Plot method for the nb function
Model > Classification and regression trees Estimate classification and regression trees
`crtree()`	Classification and regression trees based on the rpart package
`summary(<crtree>)`	Summary method for the crtree function
`predict(<crtree>)`	Predict method for the crtree function
`print(<crtree.predict>)`	Print method for predict.crtree
`plot(<crtree>)`	Plot method for the crtree function
`cv.crtree()`	Cross-validation for Classification and Regression Trees
Model > Random Forest Estimate a random forest of classification or regression trees
`rforest()`	Random Forest using Ranger
`summary(<rforest>)`	Summary method for the rforest function
`predict(<rforest>)`	Predict method for the rforest function
`print(<rforest.predict>)`	Print method for predict.rforest
`plot(<rforest.predict>)`	Plot method for rforest.predict function
`store(<rforest.predict>)`	Store predicted values generated in the rforest function
`plot(<rforest>)`	Plot method for the rforest function
`cv.rforest()`	Cross-validation for a Random Forest
Model > Gradient Boosted Trees Estimate a gradient boosted trees for regression of classification
`gbt()`	Gradient Boosted Trees using XGBoost
`summary(<gbt>)`	Summary method for the gbt function
`predict(<gbt>)`	Predict method for the gbt function
`print(<gbt.predict>)`	Print method for predict.gbt
`plot(<gbt>)`	Plot method for the gbt function
`cv.gbt()`	Cross-validation for Gradient Boosted Trees
Model > Evaluate regression Evaluate regression models
`evalreg()`	Evaluate the performance of different regression models
`summary(<evalreg>)`	Summary method for the evalreg function
`plot(<evalreg>)`	Plot method for the evalreg function
`MAE()`	Mean Absolute Error
`RMSE()`	Root Mean Squared Error
`Rsq()`	R-squared
`profit()`	Calculate Profit based on cost:margin ratio
Model > Evaluate classification Evaluate binary classification models
`evalbin()`	Evaluate the performance of different (binary) classification models
`summary(<evalbin>)`	Summary method for the evalbin function
`plot(<evalbin>)`	Plot method for the evalbin function
`confusion()`	Confusion matrix
`summary(<confusion>)`	Summary method for the confusion matrix
`plot(<confusion>)`	Plot method for the confusion matrix
`auc()`	Area Under the Curve (AUC)
`rig()`	Relative Information Gain (RIG)
Model > Collaborative filtering Esitmate collaborative filtering models
`crs()`	Collaborative Filtering
`summary(<crs>)`	Summary method for Collaborative Filter
`plot(<crs>)`	Plot method for the crs function
Model > Decision analysis Create and evaluate decision trees
`dtree()`	Create a decision tree
`summary(<dtree>)`	Summary method for the dtree function
`plot(<dtree>)`	Plot method for the dtree function
`sensitivity()`	Method to evaluate sensitivity of an analysis
`sensitivity(<dtree>)`	Evaluate sensitivity of the decision tree
`dtree_parser()`	Parse yaml input for dtree to provide (more) useful error messages
Model > Simulate Create simulation models
`simulater()`	Simulate data for decision analysis
`summary(<simulater>)`	Summary method for the simulater function
`plot(<simulater>)`	Plot method for the simulater function
`repeater()`	Repeated simulation
`summary(<repeater>)`	Summarize repeated simulation
`plot(<repeater>)`	Plot repeated simulation
`sim_summary()`	Print simulation summary
`sdw()`	Standard deviation of weighted sum of variables
`sim_cleaner()`	Clean input command string
`sim_splitter()`	Split input command string
`sim_cor()`	Simulate correlated normally distributed data
`find_max()`	Find maximum value of a vector
`find_min()`	Find minimum value of a vector
General modeling functions General modeling functions
`plot(<model.predict>)`	Plot method for model.predict functions
`scale_df()`	Center or standardize variables in a data frame
`minmax()`	Calculate min and max before standardization
`onehot()`	One hot encoding of data.frames
`predict_model()`	Predict method for model functions
`print_predict_model()`	Print method for the model prediction
`store(<model>)`	Store residuals from a model
`store(<model.predict>)`	Store predicted values generated in model functions
`render(<DiagrammeR>)`	Method to render DiagrammeR plots
`test_specs()`	Add interaction terms to list of test variables if needed
`var_check()`	Check if main effects for all interaction effects are included in the model
`write.coeff()`	Write coefficient table for linear and logistic regression
Starting radiant.model Functions used to start radiant shiny apps
`radiant.model()`	radiant.model
`radiant.model_viewer()`	Launch radiant.model in the Rstudio viewer
`radiant.model_window()`	Launch radiant.model in an Rstudio window
Data sets Data sets bundled with radiant.model
`catalog`	Catalog sales for men's and women's apparel
`dvd`	Data on DVD sales
`direct_marketing`	Direct marketing data
`houseprices`	Houseprices
`ideal`	Ideal data for linear regression
`ketchup`	Data on ketchup choices
`movie_contract`	Movie contract decision tree
`ratings`	Movie ratings
Deprecated Deprecated
`ann()`	Deprecated function(s) in the radiant.model package
`store(<crs>)`	Deprecated: Store method for the crs function

Reference

Model > Linear regression (OLS)

Model > Logistic regression

Model > Multinomial logistic regression

Model > Neural network

Model > Naive Bayes

Model > Classification and regression trees

Model > Random Forest

Model > Gradient Boosted Trees

Model > Evaluate regression

Model > Evaluate classification

Model > Collaborative filtering

Model > Decision analysis

Model > Simulate

General modeling functions

Starting radiant.model

Data sets

Deprecated