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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.
install.packages("folda")You can install the development version of folda from GitHub with:
# install.packages("devtools")
devtools::install_github("Moran79/folda")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!
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).
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 0.05194279Plot the results:
plot(fit, datX = datX, response = response)
One-dimensional plot:
# A 1D plot is created when there is only one feature
# or for binary classification problems.
mpgSmall <- mpg[, c("cyl", "displ")]
fitSmall <- folda(mpgSmall[, -1, drop = FALSE], mpgSmall[, 1])
plot(fitSmall, mpgSmall, mpgSmall[, 1])
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-06More examples can be found in the vignette.
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.
Wang, S. (2024). A new forward discriminant analysis framework based on Pillai’s trace and ULDA. arXiv preprint, arXiv:2409.03136. Retrieved from https://arxiv.org/abs/2409.03136.
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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