R: Newton-Raphson method to fit SCAM

scam.fit {scam} | R Documentation

## Newton-Raphson method to fit SCAM

### Description

This routine estimates SCAM coefficients given log smoothing parameters using the Newton-Raphson method. The estimation of the smoothing parameters by the GCV/UBRE score optimization is outer to the model fitting. Routine `gcv.ubre_grad` evaluates the first derivatives of the smoothness selection scores with respect to the log smoothing parameters. Routine `bfgs_gcv.ubre` estimates the smoothing parameters using the BFGS method.

The function is not normally called directly, but rather service routines for `scam`.

### Usage

    scam.fit(G,sp, gamma=1, etastart=NULL, mustart=NULL, env=env,
                  null.coef=rep(0,ncol(G$X)), control=scam.control())

### Arguments

`G` | A list of items needed to fit a SCAM.
`sp` | The vector of smoothing parameters.
`gamma` | This constant allows to inflate the model degrees of freedom in the GCV or UBRE/AIC score.

`etastart` | Initial values for the linear predictor.
`mustart` | Initial values for the expected values.
`env` | Get the enviroment for the model coefficients, their derivatives and the smoothing parameter.
`null.coef` | coefficients for a null model, needed for an ability to check for immediate divergence.
`control` | A list of fit control parameters returned by `scam.control`. It includes: `maxit`, a positive scalar which gives the maximum number of iterations for Newton's method; `devtol.fit`, a scalar giving the tolerance at which the relative penalized deviance is considered to be close enougth to 0 to terminate the algorithm; `steptol.fit`, a scalar giving the tolerance at which the scaled distance between two successive iterates is considered close enough to zero to terminate the algorithm; `trace` turns on or off some de-bugging information; `print.warn`, when set to `FALSE` turns off printing warning messages for step halving under non-finite exponentiated coefficients, non-finite deviance and/or if `mu` or `eta` are out of bounds.

### Details

The routine applies step halving to any step that increases the penalized deviance substantially.

### Author(s)

Natalya Pya <nat.pya@gmail.com>

### References

Pya, N. and Wood, S.N. (2015) Shape constrained additive models. Statistics and Computing, 25(3), 543-559

Pya, N. (2010) Additive models with shape constraints. PhD thesis. University of Bath. Department of Mathematical Sciences

Wood, S.N. (2008) Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society (B) 70(3):495-518

Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36

### See Also

`scam`

[Package scam version 1.2-12 Index]