Description Usage Arguments Details Value Author(s) References See Also

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

1 2 3 4 5 |

`object` |
a list of vectors of observations |

`tme` |
a list of vectors of time points corresponding to the observations in |

`ind` |
a list of factors (or vectors that can be coerced to factors) of subject identifiers
corresponding to the observations in |

`verbose` |
if |

`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 |

`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 |

`maxIter` |
maximum number of iterations to be performed for the EM algorithm |

`knots` |
location of spline knots. If |

`zeroIntercept` |
experimental feature. If |

`deltaEM` |
convergence tolerance for the EM algorithm |

`deltaNM` |
(relative) convergence tolerance for the Nelder-Mead optimisation |

`criteria` |
one of |

`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 |

`...` |
additional arguments, currently not used |

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.

A list of objects of class `sme`

. See `smeObject`

for the components of the fit and
`plot.sme`

for visualisation options

Maurice Berk [email protected]

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

`smeObject`

, `sme`

, `sme.data.frame`

,
`plot.sme`

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.