slm {assist} | R Documentation |

## Fit a Semi-parametric Linear Mixed Effects Model

### Description

Returns an object of class `slm`

that represents a
semi-parametric linear mixed effects model fit.

### Usage

```
slm(formula, rk, data=list(), random, weights=NULL,
correlation=NULL, control=list(apVar=FALSE))
```

### Arguments

`formula` |
a formula object, with the response on the left of a |

`rk` |
a list of expressions that specify the reproducing kernels of the spline function(s), |

`data` |
An optional data frame containing the variables appearing in |

`random` |
A named list of formulae, lists of formulae, or pdMat objects, which defines nested random effects structures. See help file of lme for more details. |

`weights` |
An optional |

`correlation` |
An optional |

`control` |
an optional list of any applicable control parameters from |

### Details

This generic function fits a semi-parametric linear mixed effects model (or non-parametric mixed effects models) as described in Wang (1998), but allowing for general random and correlation structures. Because the connection to a linear mixed effects model is adopted, only GML is available to choose smoothing parameters.

### Value

An object of class `slm`

is returned. Generic functions such as print, summary, predict and intervals have
methods to show the results of the fit.

Note: output from earlier versions of `slm`

shows incorrect smoothing spline parameters for SSANOVA, which is corrected in this version.

### Author(s)

Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu.

### References

Wang, Y. (1998) Mixed Effects Smoothing Spline ANOVA. JRSS, Series B, 60:159–174.

Pinherio, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-Plus. Springer.

### See Also

`ssr`

, `predict.slm`

, `intervals.slm`

,
`print.slm`

,`summary.slm`

### Examples

```
## Not run:
## SS ANOVA is used to model "time" and "group"
## with random intercept for "dog".
data(dog)
dog.fit<- slm(y~group*time, rk=list(cubic(time), shrink1(group),
rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time),
shrink1(group))), random=list(dog=~1), data=dog)
## End(Not run)
```

*assist*version 3.1.9 Index]