sme (version 0.4)

sme: Smoothing-splines mixed-effects models

Description

This generic function fits a smoothing-splines mixed-effects model

Usage

sme(object,tme,ind,verbose=F,lambda.mu=NULL,lambda.v=NULL,maxIter=500,
knots=NULL,zeroIntercept=F,deltaEM=1e-3,deltaNM=1e-3,criteria="AICc",...)

Arguments

object

either a vector of observations, a data.frame object or a list of vectors of observations. The method functions sme.data.frame and sme.list are documented separately

tme

either a vector of time points corresponding to the observations given in object or a list of vectors of time points in the case of sme.list. Ignored in the case of sme.data.frame

ind

a factor (or a vector that can be coerced to a factor) of subject identifiers corresponding to the observations given in object or a list of factors of subject identifiers in the case of sme.list. Ignored in the case of <

verbose

if TRUE, debug information will be output while fitting the model

lambda.mu

smoothing parameter used for the fixed-effect function. If NULL, the optimal values for this and lambda.v will be found according to criteria using Nelder-Mead search

lambda.v

smoothing parameter used for the random-effects functions. If NULL, the optimal values for this and lambda.mu will be found according to criteria using Nelder-Mead search

maxIter

maximum number of iterations to be performed for the EM algorithm

knots

location of spline knots. If NULL, an incidence matrix representation will be used. See `Details'

zeroIntercept

experimental feature. If TRUE, the fitted values of the fixed- and random-effects functions at the intercept will be zero

deltaEM

convergence tolerance for the EM algorithm

deltaNM

(relative) convergence tolerance for the Nelder-Mead optimisation

criteria

one of "AICc", "AIC", "BICN" or "BICn" indicating which criteria to use to score a particular combination of lambda.mu and lambda.v in the Nelder-Mead search

...

additional arguments to sme.data.frame or sme.list

Value

• An object of class sme representing the smoothing-splines mixed-effects model fit. See smeObject for the components of the fit and plot.sme for visualisation options

Details

The default behaviour is to use an incidence matrix representation for the smoothing-splines. This works well in most situations but may incur a high computational cost when the number of distinct time points is large, as may be the case for irregularly sampled data. Alternatively, a basis projection can be used by giving a vector of knots of length (much) less than the number of distinct time points.

References

Berk, M. (2012). Smoothing-splines Mixed-effects Models in R. Preprint

See Also

smeObject, sme.data.frame, sme.list, plot.sme

Examples

data(MTB)
  fit.AIC <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="AIC")
  fit.BICN <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="BICN")
  fit.BICn <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="BICn")
  fit.AICc <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="AICc")

  fit <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],lambda.mu=1e5,lambda.v=1e5)

  data(inflammatory)
  system.time(fit <- sme(inflammatory,deltaEM=0.1,deltaNM=0.1))
  system.time(fit <- sme(inflammatory,knots=c(29.5,57,84.5),deltaEM=0.1,deltaNM=0.1))