slm {assist}

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 \sim operator, and the bases of the null space H_0 of the non-parametric function and other terms, separated by + operators, on the right.

rk

a list of expressions that specify the reproducing kernels of the spline function(s), R^1,\dots,R^p for spaces H_1,\dots,H_p. See the help file of ssr for more details.

data

An optional data frame containing the variables appearing in formula, random, rk, correlation, weights. By default, the variables are taken from the environment from which slm is called.

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 varFun object or one-sided formula describing the within-group heteroscedasticity stucture. See the help file of lme for more details.

correlation

An optional corStruct object specifying the within-group correlation structure. See lme for more details.

control

an optional list of any applicable control parameters from lme.

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 [e-mail] and Yuedong Wang [e-mail].

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)