Description Usage Arguments Details Value Author(s) References See Also Examples
Carry out one or more independent smoothing-splines mixed-effects model fits simultaneously
1 2 3 4 |
object |
a |
tme |
for consistency with the generic function. Ignored in this case |
ind |
for consistency with the generic function. Ignored in this case |
verbose |
if |
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 |
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 |
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 used when carrying out multiple fits, specifically
|
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.
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
Maurice Berk maurice@mauriceberk.com
Berk, M. (2012). Smoothing-splines Mixed-effects Models in R. Preprint
smeObject
, sme
, sme.list
, plot.sme
1 2 3 4 5 6 | ## 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)
|
Loading required package: splines
Loading required package: lattice
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.