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

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

[Package sme version 0.8 Index]