SOP-package {SOP}

Generalised Additive P-Spline Regression Models Estimation

Description

Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.

Details

Index of help topics:

SOP-package             Generalised Additive P-Spline Regression Models
                        Estimation
ad                      Adaptive smooth terms in a SOP model formula
f                       Defining smooth terms in SOP formulae
plot.sop                Default SOP plotting
predict.sop             Prediction from a fitted SOP model
print.sop               Print method for sop objects
rae                     Defining random effects in SOP formula
sop                     Estimation of generalised additive P-spline
                        regression models with overlapping penalties.
sop.control             Function for controlling SOP fitting
sop.fit                 Fitting generalised linear mixed models with
                        overlapping precision matrices.
summary.sop             Summary method for a fitted SOP model.

This package incorporates the function sop() which enables users to estimate multidimensional generalised P-spline regression models with overlapping penalties. For a complete list of functions use library(help = SOP).

Author(s)

NA

Maintainer: Maria Xose Rodriguez-Alvarez <[e-mail]>

References

Eilers, P.H.C. and Marx, B.D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11 (2), 89–121.

Rodriguez-Alvarez, M.X., Lee, D. J., Kneib, T., Durban, M., and Eilers, P. (2015). Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm. Statistics and Computing, 25 (5), 941–957.

Rodriguez-Alvarez, M.X., Durban, M., Lee, D. J. and Eilers, P. (2019). On the estimation of variance parameters in non-standard generalised linear mixed models: application to penalised smoothing. Statistics and Computing, 29 (3), 483–500.