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