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",
    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

### 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.

### 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

### See Also

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

[Package sme version 1.0.2 Index]