bigsplines-package: Smoothing Splines for Large Samples

Description Details Author(s) References Examples

Description

Fits smoothing spline regression models using scalable algorithms designed for large samples. Six marginal spline types are supported: cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric predictors are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Details

The function bigspline fits one-dimensional cubic smoothing splines (unconstrained or periodic). The function bigssa fits Smoothing Spline Anova (SSA) models (Gaussian data). The function bigssg fits Generalized Smoothing Spline Anova (GSSA) models (non-Gaussian data). The function bigssp is for fitting Smoothing Splines with Parametric effects (semi-parametric regression). The function bigtps fits one-, two-, and three-dimensional cubic thin-plate splines. There are corresponding predict, print, and summary functions for these methods.

Author(s)

Nathaniel E. Helwig <[email protected]>

Maintainer: Nathaniel E. Helwig <[email protected]>

References

Gu, C. (2013). Smoothing spline ANOVA models, 2nd edition. New York: Springer.

Gu, C. and Wahba, G. (1991). Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM Journal on Scientific and Statistical Computing, 12, 383-398.

Gu, C. and Xiang, D. (2001). Cross-validating non-Gaussian data: Generalized approximate cross-validation revisited. Journal of Computational and Graphical Statistics, 10, 581-591.

Helwig, N. E. (2013). Fast and stable smoothing spline analysis of variance models for large samples with applications to electroencephalography data analysis. Unpublished doctoral dissertation. University of Illinois at Urbana-Champaign.

Helwig, N. E. (2016). Efficient estimation of variance components in nonparametric mixed-effects models with large samples. Statistics and Computing, 26, 1319-1336.

Helwig, N. E. and Ma, P. (2015). Fast and stable multiple smoothing parameter selection in smoothing spline analysis of variance models with large samples. Journal of Computational and Graphical Statistics, 24, 715-732.

Helwig, N. E. and Ma, P. (2016). Smoothing spline ANOVA for super-large samples: Scalable computation via rounding parameters. Statistics and Its Interface, 9, 433-444.

Examples

1
# See examples for bigspline, bigssa, bigssg, bigssp, and bigtps

taylerablake/thin-plate-splines documentation built on Sept. 19, 2017, 9:45 a.m.