Description Usage Arguments Details Value Side Effects References See Also Examples
This function estimates nonparametrically the mean profile from a matrix
y
which is assumed to contain repeated measurements (i.e. longitudinal
data) from a set of individuals.
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y 
matrix containing the values of the response variable, with rows associated to individuals and columns associated to observation times. 
Time 
a vector containing the observation times of the response variable, assumed
to be the same for all individuals of matrix 
minh 
the minimum value of the interval where the optimal value of the smoothing parameter is searched according to the modified Rice criterion. See reference below for details. 
maxh 
the maximum value of the above interval. 
optimize 
Logical value, default is 
rice.display 
If this set to 
... 
other optional parameters are passed to the

see Section 7.4 of the reference below.
a list containing the returned value produced by sm.regression
when
smoothing the mean response value at each given observation time,
with an extra component $aux
added to the list.
This additional component is a list itself containing the mean value at each
observation time, the residual variance of the residuals from the estimated
regression curve, the autocorrelation function of the residuals, and
the value h
of the chosen smoothing parameter.
if the parameter display is not set to "none"
, a plot of the estimated
regression curve is produced;
other aspects are controlled by the optional parameters (...
).
If rice.display=TRUE
, a plot of the modified Rice criterion is shown.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with SPlus Illustrations. Oxford University Press, Oxford.
sm.regression
, sm.regression.autocor
, optim
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