R: Smoothing-splines mixed-effects models

sme {sme}

R Documentation

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 `sme.data.frame`
`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`

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.

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

Author(s)

Maurice Berk maurice.berk01@imperial.ac.uk

References

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

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,knots=c(29.5,57,84.5),deltaEM=0.1,deltaNM=0.1))

[Package sme version 0.8 Index]