Package index • tidyfit

Basic functionality

The functions regress() and classify() provide basic modeling wrappers for tidy data.

regress: Linear regression on tidy data

classify: Classification on tidy data

Generic methods

The ‘tidyfit.models’ frame produced by ‘regress()’ or ‘classify()’ can be used to obtain coefficients or predictions.

coef(<tidyfit.models>): Extract coefficients from a tidyfit.models frame

explain(<tidyfit.models>): An interface for variable importance measures for a fitted tidyfit.models frames

explain(): An interface for variable importance measures for a fitted tidyfit.models frames

predict(<tidyfit.models>): Predict using a tidyfit.models frame

fitted(<tidyfit.models>): Obtain fitted values from models in a tidyfit.models frame

residuals(<tidyfit.models>): Obtain residuals from models in a tidyfit.models frame

Modeling engine

The model wrapper m() standardizes a large number of linear regression and classification algorithms.

m(): Generic model wrapper for tidyfit

Implemented algorithms

.fit(<adalasso>): Adaptive Lasso regression or classification for tidyfit

.fit(<anova>): ANOVA for tidyfit

.fit(<bayes>): Bayesian generalized linear regression for tidyfit

.fit(<blasso>): Bayesian Lasso regression for tidyfit

.fit(<bma>): Bayesian model averaging for tidyfit

.fit(<boost>): Gradient boosting regression for tidyfit

.fit(<bridge>): Bayesian ridge regression for tidyfit

.fit(<chisq>): Pearson's Chi-squared test for tidyfit

.fit(<cor>): Pearson's correlation for tidyfit

.fit(<enet>): ElasticNet regression or classification for tidyfit

.fit(<genetic>): Genetic algorithm with linear regression fitness evaluator for tidyfit

.fit(<gets>): General-to-specific regression for tidyfit

.fit(<glm>): Generalized linear regression for tidyfit

.fit(<glmm>): Generalized linear mixed-effects model for tidyfit

.fit(<group_lasso>): Grouped Lasso regression and classification for tidyfit

.fit(<hfr>): Hierarchical feature regression for tidyfit

.fit(<lasso>): Lasso regression and classification for tidyfit

.fit(<lm>): Linear regression for tidyfit

.fit(<mrmr>): Minimum redundancy, maximum relevance feature selection for tidyfit

.fit(<mslm>): Markov-Switching Regression for tidyfit

.fit(<nnet>): Neural Network regression for tidyfit

.fit(<pcr>): Principal Components Regression for tidyfit

.fit(<plsr>): Partial Least Squares Regression for tidyfit

.fit(<quantile>): Quantile regression for tidyfit

.fit(<quantile_rf>): Quantile regression forest for tidyfit

.fit(<relief>): ReliefF and RReliefF feature selection algorithm for tidyfit

.fit(<rf>): Random Forest regression or classification for tidyfit

.fit(<ridge>): Ridge regression and classification for tidyfit

.fit(<robust>): Robust regression for tidyfit

.fit(<spikeslab>): Bayesian Spike and Slab regression or classification for tidyfit

.fit(<subset>): Best subset regression and classification for tidyfit

.fit(<svm>): Support vector regression or classification for tidyfit

.fit(<tvp>): Bayesian Time-Varying Regression for tidyfit

Data

tidyfit comes with a built-in financial factor returns dataset.

Factor_Industry_Returns: Industry-Factor Returns Data Set