Description Usage Arguments Details Value Author(s) References See Also Examples
Carry out one or more independent smoothingsplines mixedeffects 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 fixedeffect function or 
lambda.v 
in the case of carrying out a single model fit, either a smoothing parameter to
be used for the randomeffects 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 NelderMead optimisation 
criteria 
one of 
initial.lambda.mu 
value to initialise the smoothing parameter for the fixedeffects to in the NelderMead search. See details below 
initial.lambda.v 
value to initialise the smoothing parameter for the randomeffects to in the NelderMead 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 NelderMead 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 smoothingsplines. 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 [email protected]
Berk, M. (2012). Smoothingsplines Mixedeffects 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)

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