This generic function fits a smoothingsplines mixedeffects model
1 2 
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 fixedeffect function. If 
lambda.v 
smoothing parameter used for the randomeffects 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 NelderMead optimisation 
criteria 
one of 
... 
additional arguments to 
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.
An object of class sme
representing the smoothingsplines mixedeffects model fit. See
smeObject
for the components of the fit and plot.sme
for visualisation options
Maurice Berk maurice.berk01@imperial.ac.uk
Berk, M. (2012). Smoothingsplines Mixedeffects 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))

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