R: Numerical Engine for ssanova and gssanova

rkpk {gss} | R Documentation

## Numerical Engine for ssanova and gssanova

### Description

Perform numerical calculations for the `ssanova` and `gssanova` suites.

### Usage

    sspreg1(s, r, q, y, wt, method, alpha, varht, random)
    mspreg1(s, r, id.basis, y, wt, method, alpha, varht, random, skip.iter)
    ngreg1(family, s, r, id.basis, y, wt, offset, method, varht, alpha, nu, random, skip.iter)

    sspreg91(s, r, q, y, cov, method, alpha, varht)
    mspreg91(s, r, id.basis, y, cov, method, alpha, varht, skip.iter)

    sspngreg(family, s, r, q, y, wt, offset, alpha, nu, random)
    mspngreg(family, s, r, id.basis, y, wt, offset, alpha, nu, random, skip.iter)
    ngreg(dc, family, sr, q, y, wt, offset, nu, alpha)

    regaux(s, r, q, nlambda, fit)

    ngreg.proj(dc, family, sr, q, y0, wt, offset, nu)

### Arguments

`family` | Description of the error distribution. Supported are exponential families `"binomial"`, `"poisson"`, `"Gamma"`, and `"nbinomial"`. Also supported are accelerated life model families `"weibull"`, `"lognorm"`, and `"loglogis"`.
`s` | Unpenalized terms evaluated at data points.
`r` | Basis of penalized terms evaluated at data points.
`q` | Penalty matrix.
`id.basis` | Index of observations to be used as "knots."
`y` | Response vector.
`wt` | Model weights.
`cov` | Input for covariance function for correlated data.
`offset` | Model offset.
`method` | `"v"` for GCV, `"m"` for GML, or `"u"` for Mallows' CL.
`alpha` | Parameter modifying GCV or Mallows' CL scores for smoothing parameter selection.
`nu` | Optional argument for future support of nbinomial, weibull, lognorm, and loglogis families.
`varht` | External variance estimate needed for `method="u"`.
`random` | Input for parametric random effects in nonparametric mixed-effect models.
`skip.iter` | Flag indicating whether to use initial values of theta and skip theta iteration.
`nlambda` | Smoothing parameter in effect.
`fit` | Fitted model.
`dc` | Coefficients of fits.
`sr` | `cbind(s,r)`.
`y0` | Components of the fit to be projected.

### Details

`sspreg1` is used by `ssanova` to compute regression estimates with a single smoothing parameter. `mspreg1` is used by `ssanova` to compute regression estimates with multiple smoothing parameters.

`ssngpreg` is used by `gssanova` to compute non-Gaussian regression estimates with a single smoothing parameter. `mspngreg` is used by `gssanova` to compute non-Gaussian regression estimates with multiple smoothing parameters. `ngreg` is used by `ssngpreg` and `mspngreg` to perform Newton iteration with fixed smoothing parameters and to calculate cross-validation scores on return.

`regaux` is used by `sspreg1`, `mspreg1`, `ssngpreg`, and `mspngreg` to obtain auxiliary information needed by `predict.ssanova` for standard error calculation.

`ngreg.proj` is used by `project.gssanova` to calculate the Kullback-Leibler projection for non-Gaussian regression.

### References

Gu, C. (1992), Cross validating non Gaussian data. Journal of Computational and Graphical Statistics, 1, 169–179.

Kim, Y.-J. and Gu, C. (2004), Smoothing spline Gaussian regression: more scalable computation via efficient approximation. Journal of the Royal Statistical Society, Ser. B, 66, 337–356.

[Package gss version 2.2-2 Index]