sme.list: Carry out mulitple independent smoothing-splines...

In sme: Smoothing-splines Mixed-effects Models

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 [e-mail]

References

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

See Also

smeObject, sme, sme.data.frame, plot.sme

sme documentation built on May 30, 2017, 3:09 a.m.