The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

SPSP: an R Package for Selecting the relevant predictors by Partitioning the Solution Paths of the Penalized Likelihood Approach

CRAN checks

Overview

An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and EBIC (Liu, Y., & Wang, P. (2018) https://doi.org/10.1214/18-EJS1434). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, SCAD, MCP, ridge regression, and other penalized estimators.

Installation

The SPSP package is currently available on SPSP CRAN.

# Install the development version from GitHub
if (!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("XiaoruiZhu/SPSP")

Install SPSP from the CRAN

# Install from CRAN
install.packages("SPSP")

Example

The user-friendly function SPSP() conducts the selection by Partitioning the Solution Paths (the SPSP procedure) to selects the relevant predictors. The user only needs to specify the independent variables matrix, response, family, and a penalized method that can generate the solution paths, for example, Lasso, adaptive Lasso, SCAD, MCP, ridge regression. The embedded selection methods in this package can be called using fitfun.SP = lasso.glmnet. Currently, six methods are included: lasso.glmnet, adalasso.glmnet, adalassoCV.glmnet, SCAD.ncvreg, MCP.ncvreg, and ridge.glmnet.

The following example shows the R codes:

library(SPSP)
data(HihgDim)
library(glmnet)

x <- as.matrix(HighDim[,-1])
y <- HighDim[,1]

# SPSP + lasso
spsp_lasso_1 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = lasso.glmnet,
                     init = 1, standardize = FALSE, intercept = FALSE)

head(spsp_lasso_1$nonzero)
head(spsp_lasso_1$beta_SPSP)

# SPSP + adalasso
spsp_adalasso_5 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = adalasso.glmnet,
                        init = 5, standardize = T, intercept = FALSE)
                              
head(spsp_adalasso_5$nonzero)
head(spsp_adalasso_5$beta_SPSP)

# SPSP + SCAD
spsp_scad_5 <- SPSP(x = x, y = y, family = "gaussian", fitfun.SP = SCAD.ncvreg,
                    init = 5, standardize = T, intercept = FALSE)
                              
head(spsp_scad_5$nonzero)
head(spsp_scad_5$beta_SPSP)

References

Liu, Y., & Wang, P. (2018). Selection by partitioning the solution paths. Electronic Journal of Statistics, 12(1), 1988-2017. <10.1214/18-EJS1434>

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.
Health stats visible at Monitor.