R: Carry out mulitple independent smoothing-splines...

sme.list {sme}

R Documentation

Carry out mulitple independent smoothing-splines mixed-effects model fits simultaneously

Description

Carry out multiple independent smoothing-splines mixed-effects model fits simultaneously

Usage

## S3 method for class 'list'
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",
numberOfThreads=-1,...)

Arguments

`object`	a list of vectors of observations
`tme`	a list of vectors of time points corresponding to the observations in `object`
`ind`	a list of factors (or vectors that can be coerced to factors) of subject identifiers corresponding to the observations in `object`
`verbose`	if `TRUE`, debug information will be output while fitting the model(s)
`lambda.mu`	either a single smoothing parameter to be used for the fixed-effect function 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 fits
`lambda.v`	either a single smoothing parameter to be used for the random-effects functions 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 fits
`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
`numberOfThreads`	The number of threads to use to fit the multiple smoothing-splines mixed-effects models simultaneously. When `numberOfThreads=-1`, as is the default, the OpenMP system will handle thread creation dynamically
`...`	additional arguments, currently not used

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

A list of objects of class sme. 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