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

1 2 3 4 |

`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 |

`numberOfThreads` |
The number of threads to use to fit the multiple smoothing-splines
mixed-effects models simultaneously. When |

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

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 maurice@mauriceberk.com

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

`smeObject`

, `sme`

, `sme.data.frame`

,
`plot.sme`

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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