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

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

1 2 3 |

`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 |

`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 to |

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.

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

Maurice Berk [email protected]

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

`smeObject`

, `sme.data.frame`

, `sme.list`

, `plot.sme`

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))
``` |

sme documentation built on Feb. 11, 2018, 3:11 p.m.

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