sme.list: 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 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",
initial.lambda.mu=10000,initial.lambda.v=10000,normalizeTime=FALSE,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

initial.lambda.mu

value to initialise the smoothing parameter for the fixed-effects to in the Nelder-Mead search. See details below

initial.lambda.v

value to initialise the smoothing parameter for the random-effects to in the Nelder-Mead search. See details below

normalizeTime

should time be normalized to lie in $[0,1]$? See details below

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

Value

A list of objects of class sme. See smeObject for the components of the fit and plot.sme for visualisation options

Details

Prior to package version 0.9, starting values for the smoothing parameters in the Nelder-Mead search were fixed to $10000$ for both lambda.mu and lambda.v. As it turns out, the appropriate scale for the smoothing parameters depends on the scale for tme and so tme will now automatically be rescaled to lie in $[0,1]$ and much smaller initial values for the smoothing parameters will be used, although these can now optionally changed to achieve best results. To reproduce results obtained using previous versions of the package, set initial.lambda.mu=10000, initial.lambda.v=10000 and normalizeTime=FALSE.

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