folda

The folda package is an R modeling tool designed for fitting Forward Stepwise Linear Discriminant Analysis (LDA) and Uncorrelated Linear Discriminant Analysis (ULDA). If you’re unfamiliar with stepwise LDA or ULDA, please refer to the following resources:

• For stepwise LDA using Wilks’ Lambda, see Section 6.11.1 in Methods of Multivariate Analysis, Third Edition by Alvin C. Rencher and William F. Christensen (2012).

• For ULDA, refer to Ye, J., & Yu, B. (2005). Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning Research, 6(4). Link.

• For a combination of ULDA and forward LDA using Pillai’s trace, see Wang, S. (2024). A New Forward Discriminant Analysis Framework Based on Pillai’s Trace and ULDA. arXiv preprint arXiv:2409.03136. Link.

Installation

install.packages("LDATree")

You can install the development version of folda from GitHub with:

# install.packages("devtools")
devtools::install_github("Moran79/folda")

Overview

If you’ve ever been frustrated by the warnings and errors from MASS::lda(), you will appreciate the ULDA implementation in folda(). It offers several key improvements:

• No more “constant within group” errors! ULDA can handle constant columns and perfect separation of groups.

• Automatic missing value handling! The implementation seamlessly integrates automatic missing value imputation during both training and testing phases.

• Fast! ULDA is implemented using the Generalized Singular Value Decomposition (GSVD) method, which diagonalizes both within-class and total scatter matrices simultaneously, offering a speed advantage over the sequential diagonalization used in MASS::lda() (see Howland et al., 2003 for more details). We have also rewritten the matrix decomposition modules (SVD, QR) using RcppEigen, further improving computational efficiency by leveraging optimized C++ code.

• Better visualization! folda uses ggplot2 to provide visualizations of class separation in projected 2D spaces (or 1D histograms), offering valuable insights.

For the forward LDA implementation, folda offers the following advantages over the classical framework:

• No issues with multicollinearity or perfect linear dependency! Since folda() is built on ULDA, it effectively solves for the scaling matrix.

• Handles perfect separation and offers greater power! The classical approach using Wilks’ Lambda has known limitations, including premature stopping when some (not all) groups are perfectly separated. Pillai’s trace, as used in folda(), not only effectively addresses perfect separation, but has also been shown to generally have greater statistical power than Wilks’ Lambda (Rencher et al., 2002).

Basic Usage

library(folda)
mpg <- as.data.frame(ggplot2::mpg) # Prepare the data
datX <- mpg[, -5] # All predictors without Y
response <- mpg[, 5] # we try to predict "cyl" (number of cylinders)

Build a ULDA model with all variables:

fit <- folda(datX = datX, response = response, subsetMethod = "all")

Build a ULDA model with forward selection via Pillai’s trace:

fit <- folda(datX = datX, response = response, subsetMethod = "forward", testStat = "Pillai")
print(fit) # 6 out of 11 variables are selected, displ is the most important among them
#> 
#> Overall Pillai's trace: 1.325
#> Associated p-value: 4.636e-74
#> 
#> Prediction Results on Training Data:
#> Refitting Accuracy: 0.9188
#> Gini Index: 0.7004
#> 
#> Confusion Matrix:
#>          Actual
#> Predicted  4  5  6  8
#>         4 69  0  3  0
#>         5  8  4  2  0
#>         6  4  0 74  2
#>         8  0  0  0 68
#> 
#> Group means of LD scores:
#>           LD1         LD2        LD3
#> 4  3.05298379  0.02700248 -0.3555829
#> 5  1.87744449 -4.45014946  0.8156167
#> 6  0.06757888  0.28356907  0.5911862
#> 8 -3.71628852 -0.09697943 -0.3023424
#> 
#> Forward Selection Results:
#>                var statOverall   statDiff  threshold
#> 1            displ    0.873393 0.87339300 0.06545381
#> 2  modelnew beetle    1.029931 0.15653777 0.05673510
#> 3       modeljetta    1.141651 0.11172064 0.05496185
#> 4 modelcaravan 2wd    1.210165 0.06851331 0.05363507
#> 5     classmidsize    1.263449 0.05328468 0.05276500
#> 6              cty    1.325255 0.06180560 0.05194279

Plot the results:

plot(fit, datX = datX, response = response)

Make predictions:

head(predict(fit, datX, type = "response"))
#> [1] "4" "4" "4" "4" "6" "4"
head(predict(fit, datX, type = "prob")) # Posterior probabilities
#>           4            5            6            8
#> 1 0.9966769 7.475058e-08 0.0033230408 7.023764e-12
#> 2 0.9994438 1.401133e-08 0.0005562131 5.338710e-13
#> 3 0.9970911 3.835722e-08 0.0029088506 1.738154e-11
#> 4 0.9983963 2.196016e-08 0.0016037009 7.365641e-12
#> 5 0.3122116 6.809673e-07 0.6877815595 6.173116e-06
#> 6 0.5995781 4.275271e-07 0.4004193019 2.123291e-06

More examples can be found in the vignette.

References

• Howland, P., Jeon, M., & Park, H. (2003). Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 25(1), 165-179.

• Rencher, A. C., & Christensen, W. F. (2002). Methods of multivariate analysis (Vol. 727). John Wiley & Sons.

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub