dr: Methods for Dimension Reduction for Regression. Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Adragni, Kofi P.; Al-Najjar, Elias; Martin, Sean; Popuri, Sai K.; Raim, Andrew M.: Group-wise sufficient dimension reduction with principal fitted components (2016)
- Xu, Rong-Fang; Lee, Shie-Jue: Dimensionality reduction by feature clustering for regression problems (2015)
- Haggag, Magda M.M.: Combining of dimension reduction regression methods (2014)
- Lindsey, Charles D.; Sheather, Simon J.; Mckean, Joseph W.: Using sliced mean variance-covariance inverse regression for classification and dimension reduction (2014)
- Yin, Xiangrong; Bura, Efstathia: Moment-based dimension reduction for multivariate response regression (2006)