R: Smoothing-splines mixed-effects model fit(s) from a...

sme.data.frame {sme}

R Documentation

Smoothing-splines mixed-effects model fit(s) from a data.frame object

Description

Carry out one or more independent smoothing-splines mixed-effects model fits simultaneously

Usage

## S3 method for class 'data.frame'
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`	a `data.frame` with named variables `y`, `tme`, `ind` and, optionally, `variable`. The first three represent observations, corresponding time points and correpsonding subjects respectively. If `variable` is missing then these are used to carry out a single model fit. If `variable` is present then it denotes variable membership, and a separate smoothing-splines mixed-effects model is fit to each unique variable
`tme`	for consistency with the generic function. Ignored in this case
`ind`	for consistency with the generic function. Ignored in this case
`verbose`	if `TRUE`, debug information will be output while fitting the model(s)
`lambda.mu`	in the case of carrying out a single model fit, either a smoothing parameter to be used for the fixed-effect function or `NULL` if the optimal values for this and `lambda.v` should be found according to `criteria` using Nelder-Mead search. For the case of multiple model fits, either a single smoothing parameter to be used for all fits, or a vector of smoothing parameters, one for each fit, or `NULL` if Nelder-Mead search should be used to find the optimal values for this and `lambda.v` for all variables
`lambda.v`	in the case of carrying out a single model fit, either a smoothing parameter to be used for the random-effects functions or `NULL` if the optimal values for this and `lambda.mu` should be found according to `criteria` using Nelder-Mead search. For the case of multiple model fits, either a single smoothing parameter to be used for all fits, or a vector of smoothing parameters, one for each fit, or `NULL` if Nelder-Mead search should be used to find the optimal values for this and `lambda.mu` for all variables
`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 used when carrying out multiple fits, specifically `numberOfThreads` indicating the number of threads used to carry out the multiple fits in parallel. See `sme.list` for details

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

In the case of a single model fit, an object of class sme. For multiple model fits, a list of such objects. See smeObject for the components of the fit and plot.sme for visualisation options

Author(s)

Maurice Berk maurice@mauriceberk.com

References

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

Examples

  data(MTB)
  system.time(fits <- sme(MTB,numberOfThreads=1))
  sapply(fits,logLik)

  system.time(fits <- sme(MTB,numberOfThreads=10))
  sapply(fits,logLik)

[Package sme version 0.8 Index]