| Type: | Package |
| Title: | Partial Profile Score Feature Selection in High-DimensionalGeneralized Linear Interaction Models |
| Version: | 0.1.1 |
| Date: | 2025-07-04 |
| Maintainer: | Zengchao Xu <zengc.xu@aliyun.com> |
| Description: | This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2022, <doi:10.4310/21-SII706>). |
| URL: | https://github.com/paradoxical-rhapsody/PPSFS |
| BugReports: | https://github.com/paradoxical-rhapsody/PPSFS/issues |
| Imports: | Rcpp, brglm2 |
| LinkingTo: | Rcpp, RcppArmadillo |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | yes |
| Packaged: | 2025-07-04 01:30:40 UTC; zengchao |
| Author: | Zengchao Xu [aut, cre], Shan Luo [aut], Zehua Chen [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-07-04 02:30:02 UTC |
PPSFS: Partial Profile Score Feature Selection in High-Dimensional Generalized Linear Interaction Models
Description
This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2022,doi:10.4310/21-SII706).
Author(s)
Maintainer: Zengchao Xuzengc.xu@aliyun.com
Authors:
Shan Luo
Zehua Chen
See Also
Useful links:
Report bugs athttps://github.com/paradoxical-rhapsody/PPSFS/issues
Partial Profile Score Feature Selection for GLMs
Description
ppsfs: PPSFS formain-effects.
ppsfsi: PPSFS forinteraction effects.
Usage
ppsfs( x, y, family, keep = NULL, I0 = NULL, ..., ebicFlag = 1, maxK = min(NROW(x) - 1, NCOL(x) + length(I0)), verbose = FALSE)ppsfsi( x, y, family, keep = NULL, ..., ebicFlag = 1, maxK = min(NROW(x) - 1, choose(NCOL(x), 2)), verbose = FALSE)Arguments
x | Matrix. |
y | Vector. |
family | |
keep | Initial set of features that are included in model fitting. |
I0 | Index set of interaction effects to be identified. |
... | Additional parameters forglm.fit. |
ebicFlag | The procedure stops when the EBIC increases after |
maxK | Maximum number of identified features. |
verbose | Print the procedure path? |
Details
Thatppsfs(x, y, family="gaussian") is an implementation tosequential lasso method proposed by Luo and Chen(2014,<\doi{10/f6kfr6}>).
Value
Index set of identified features.
References
Z. Xu, S. Luo and Z. Chen (2022). Partial profile score feature selection inhigh-dimensional generalized linear interaction models. Statistics and Its Interface.doi:10.4310/21-SII706
Examples
## ***************************************************## Identify main-effect features## ***************************************************set.seed(2022)n <- 300p <- 1000x <- matrix(rnorm(n*p), n)eta <- drop( x[, 1:3] %*% runif(3, 1.0, 1.5) )y <- eta + rnorm(n, sd=sd(eta)/5)print( A <- ppsfs(x, y, 'gaussian', verbose=TRUE) )## ***************************************************## Identify interaction effects## ***************************************************set.seed(2022)n <- 300p <- 150x <- matrix(rnorm(n*p), n)eta <- drop( cbind(x[, 1:3], x[, 4:6]*x[, 7:9]) %*% runif(6, 1.0, 1.5) )y <- eta + rnorm(n, sd=sd(eta)/5)print( group <- ppsfsi(x, y, 'gaussian', verbose=TRUE) )print( A <- ppsfs(x, y, "gaussian", I0=group, verbose=TRUE) )print( A <- ppsfs(x, y, "gaussian", keep=c(1, "5:8"), I0=group, verbose=TRUE) )