SLOPE 0.2.0

April 13, 2020
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Introduction to SLOPE

SLOPE (Bogdan et al. 2015) stands for sorted L1 penalized estimation and
is a generalization of OSCAR (Bondell and Reich 2008). As the name
suggests, SLOPE
is a type of \(\ell_1\)-regularization. More specifically, SLOPE fits
generalized linear models regularized with the sorted \(\ell_1\) norm. The
objective in SLOPE is
\[
\operatorname{minimize}\left\{ f(\beta) + J(\beta \mid \lambda)\right\},
\]

where \(f(\beta)\) is typically the log-likelihood of some model in the
family of generalized linear models and
\(J(\beta\mid \lambda) = \sum_{i=1}^p \lambda_i|\beta|_{(i)}\) is the
sorted \(\ell_1\) norm.

Some people will note that this penalty is a generalization
of the standard \(\ell_1\) norm penalty1. As such,
SLOPE is a type of sparse regression—just like the lasso. Unlike the lasso,
however, SLOPE gracefully handles correlated features.
Whereas the lasso often discards all but a few among a set of
correlated features (Jia and Yu 2010),
SLOPE instead clusters such features together by setting such clusters to
have the same coefficient in absolut value.

SLOPE 0.2.0

SLOPE 0.2.0 is a new verison of the R package
SLOPE featuring a range of
improvements over the previous package. If you are completely new to the
package, please start with the introductory vignette.

More model families

Previously, SLOPE only features ordinary least-squares regression. Now the
package features logistic, Poisson, and multinomial regression on top of that.
Just as in other similar packages, this is enabled simply by
setting family = "binomial" for logistic regression, for instance.

library(SLOPE)
fit <- SLOPE(wine$x, wine$y, family = "multinomial")

Regularization path fitting

By default, SLOPE now fits a full regularization path instead of
only a single penalty sequence at once. This behavior is now analogous with the
default behavior in glmnet.

plot(fit)

Coefficients from the regularization path for a multinomial model.

Figure 1: Coefficients from the regularization path for a multinomial model.

Predictor screening rules

The package now uses predictor screening rules to vastly improve performance
in the \(p \gg n\) domain. Screening rules are part of what makes
other related packages such as glmnet so efficient. In SLOPE, we use a
variant of the strong screening rules for the lasso (Tibshirani et al. 2012).

xy <- SLOPE:::randomProblem(100, 1000)
system.time({SLOPE(xy$x, xy$y, screen = TRUE)})
##    user  system elapsed 
##   12.95   12.30    3.61
system.time({SLOPE(xy$x, xy$y, screen = FALSE)})
##    user  system elapsed 
##    79.1    65.3    20.3

Cross-validation and caret

There is now a function trainSLOPE(), which can be used to run
cross-validation for optimal selection of sigma and q. Here, we run
8-fold cross-validation repeated 5 times.

# 8-fold cross-validation repeated 5 times
tune <- trainSLOPE(subset(mtcars, select = c("mpg", "drat", "wt")),
                   mtcars$hp,
                   q = c(0.1, 0.2),
                   number = 8,
                   repeats = 5)
plot(tune)

Cross-validation with SLOPE.

Figure 2: Cross-validation with SLOPE.

In addition, the package now also features a function caretSLOPE() that
can be used via the excellent caret package, which enables a swath
of resampling methods and comparisons.

C++ and ADMM

All of the performance-critical code for SLOPE has been rewritten in
C++. In addition, the package now features an ADMM solver for
family = "gaussian", enabled by setting solver = "admm" in the call
to SLOPE(). Preliminary testing shows that this solver is faster for
many designs, particularly when there is high correlation among predictors.

Sparse design matrices

SLOPE now also allows sparse design matrcies of classes from the Matrix package.

And much more…

For a full list of changes, please
see the changelog.

References

Bogdan, Małgorzata, Ewout van den Berg, Chiara Sabatti, Weijie Su, and Emmanuel J. Candès. 2015. “SLOPE – Adaptive Variable Selection via Convex Optimization.” The Annals of Applied Statistics 9 (3): 1103–40. https://doi.org/10/gfgwzt.

Bondell, Howard D., and Brian J. Reich. 2008. “Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR.” Biometrics 64 (1): 115–23. https://doi.org/10.1111/j.1541-0420.2007.00843.x.

Jia, J., and B. Yu. 2010. “On Model Selection Consistency of the Elastic Net When P \(>>\) N.” Statistica Sinica 20 (2): 595–611.

Tibshirani, Robert, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, and Ryan J. Tibshirani. 2012. “Strong Rules for Discarding Predictors in Lasso-Type Problems.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 74 (2): 245–66. https://doi.org/10/c4bb85.


  1. Simply set \(\lambda_i = \lambda_j\) for
    all \(i,j \in \{1,\dots,p\}\) and you get the lasso penalty.↩︎

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