slm {assist}  R Documentation 
Returns an object of class slm
that represents a
semiparametric linear mixed effects model fit.
slm(formula, rk, data=sys.parent(), random, weights=NULL, correlation=NULL, control=<see below>)
formula 
a formula object, with the response on the left of a ~ operator, and the bases of the null space H_0 of the nonparametric 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,...,R^p for spaces H_1,...,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 onesided formula describing the withingroup heteroscedasticity stucture.
See the help file of lme for more details.

correlation 
An optional corStruct object specifying the withingroup correlation structure. See lme for more details.

control 
an optional list of any applicable control parameters from lme .

This generic function fits a semiparametric linear mixed effects model (or nonparametric 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.
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.
Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu.
Wang, Y. (1998) Mixed Effects Smoothing Spline ANOVA. JRSS, Series B, 60:159–174.
Pinherio, J. C. and Bates, D. M. (2000) Mixedeffects Models in S and SPlus. Springer.
ssr
, lme
, varFunc
,
corClasses
, predict.slm
, intervals.slm
,
print.slm
,summary.slm
## 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(time0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog)