R: Nonparametric analysis of repeated measurements data
sm.rm
R Documentation
Nonparametric analysis of repeated measurements data
Description
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.
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 y.
If Time is not given, this is assumed to be 1:ncol(y).
minh
the mimimum value of the interval where the optimal value of the smoothing
parameter is seached according to the modified Rice criterion.
See reference below for details.
maxh
the maximum value of the above interval.
optimize
Logical value, default is optimize=FALSE. If
optimize=TRUE, then a full
optimization is performed after searching the interval (minh,maxh)
using the optimizer optim.
rice.display
If this set to TRUE (default is FALSE), a plot is
produced of the curve
representing the modified Rice criterion for bandwidth selection.
See reference below for details.
...
other optional parameters are passed to the sm.options
function, through a mechanism which limits their effect only to this
call of the function; those relevant for this function are the following:
add
logical value, default is add=FALSE. If add=TRUE and
display is not set to "none", then graphical output added
to the existing plot, rather than starting a new one.
display
character value controlling the amount of graphical output of the estimated
regression curve. It has the same meaning as in sm.regression.
Default value is display="lines".
ngrid
the number of divisions of the above interval to be considered.
Default: ngrid=20.
poly.index
overall degree of locally-fitted polynomial, as used by
sm.regression. Default: ngrid=1.
Details
see Section 7.4 of the reference below.
Value
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.
Side Effects
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.
References
Bowman, A.W. and Azzalini, A. (1997).
Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations.
Oxford University Press, Oxford.