| bigsplines-package {bigsplines} | R Documentation |
Smoothing Splines for Large Samples
Description
Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
Details
The DESCRIPTION file:
| Package: | bigsplines |
| Type: | Package |
| Title: | Smoothing Splines for Large Samples |
| Version: | 1.1-1 |
| Date: | 2018-05-25 |
| Author: | Nathaniel E. Helwig <helwig@umn.edu> |
| Maintainer: | Nathaniel E. Helwig <helwig@umn.edu> |
| Depends: | quadprog |
| Imports: | stats, graphics, grDevices |
| Description: | Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial. |
| License: | GPL (>=2) |
Index of help topics:
bigspline Fits Smoothing Spline
bigsplines-package Smoothing Splines for Large Samples
bigssa Fits Smoothing Spline ANOVA Models
bigssg Fits Generalized Smoothing Spline ANOVA Models
bigssp Fits Smoothing Splines with Parametric Effects
bigtps Fits Cubic Thin-Plate Splines
binsamp Bin-Samples Strategic Knot Indices
imagebar Displays a Color Image with Colorbar
makessa Makes Objects to Fit Smoothing Spline ANOVA
Models
makessg Makes Objects to Fit Generalized Smoothing
Spline ANOVA Models
makessp Makes Objects to Fit Smoothing Splines with
Parametric Effects
ordspline Fits Ordinal Smoothing Spline
plotbar Generic X-Y Plotting with Colorbar
plotci Generic X-Y Plotting with Confidence Intervals
predict.bigspline Predicts for "bigspline" Objects
predict.bigssa Predicts for "bigssa" Objects
predict.bigssg Predicts for "bigssg" Objects
predict.bigssp Predicts for "bigssp" Objects
predict.bigtps Predicts for "bigtps" Objects
predict.ordspline Predicts for "ordspline" Objects
print.bigspline Prints Fit Information for bigsplines Model
ssBasis Smoothing Spline Basis for Polynomial Splines
summary.bigspline Summarizes Fit Information for bigsplines Model
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 <helwig@umn.edu>
Maintainer: Nathaniel E. Helwig <helwig@umn.edu>
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. (2017). Regression with ordered predictors via ordinal smoothing splines. Frontiers in Applied Mathematics and Statistics, 3(15), 1-13.
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
# See examples for bigspline, bigssa, bigssg, bigssp, and bigtps