# Smoothing-splines mixed-effects models

### Description

This generic function fits a smoothing-splines mixed-effects model

### Usage

1 2 |

### Arguments

`object` |
either a vector of observations, a |

`tme` |
either a vector of time points corresponding to the observations given in |

`ind` |
a factor (or a vector that can be coerced to a factor) of subject identifiers
corresponding to the observations given in |

`verbose` |
if |

`lambda.mu` |
smoothing parameter used for the fixed-effect function. If |

`lambda.v` |
smoothing parameter used for the random-effects functions. If |

`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 |

`...` |
additional arguments to |

### 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

An object of class `sme`

representing the smoothing-splines mixed-effects model fit. See
`smeObject`

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

for visualisation options

### Author(s)

Maurice Berk maurice.berk01@imperial.ac.uk

### References

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

### See Also

`smeObject`

, `sme.data.frame`

, `sme.list`

, `plot.sme`

### Examples

1 2 3 4 5 6 7 8 9 10 | ```
data(MTB)
fit.AIC <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="AIC")
fit.BICN <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="BICN")
fit.BICn <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="BICn")
fit.AICc <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],criteria="AICc")
fit <- sme(MTB[MTB$variable==6031,c("y","tme","ind")],lambda.mu=1e5,lambda.v=1e5)
data(inflammatory)
system.time(fit <- sme(inflammatory,knots=c(29.5,57,84.5),deltaEM=0.1,deltaNM=0.1))
``` |