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.
SPSP package is currently available on SPSP CRAN.
# Install from CRAN install.packages("SPSP")
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:
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)