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",
    initial.lambda.mu=10000,initial.lambda.v=10000,normalizeTime=FALSE,...)

### 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
`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
`...` | 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

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

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

### See Also

`smeObject`, `sme`, `sme.list`, `plot.sme`

### Examples

      ## Not run: data(MTB)
      ## Not run: system.time(fits <- sme(MTB,numberOfThreads=1))
      ## Not run: sapply(fits,logLik)

      ## Not run: system.time(fits <- sme(MTB,numberOfThreads=10))
      ## Not run: sapply(fits,logLik)

[Package sme version 1.0.2 Index]