Nonparametric analysis of repeated measurements data
Usage
sm.rm(Time, y, minh=0.1, maxh=2, ngrid=20, optimize=F, display="lines",
add=F, poly.index=1, display.rice=F, ...)
Arguments
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 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.
|
ngrid
|
the number of divisions of the above interval to be considered.
|
optimize
|
Logical value, default is optimize=F . If optimize=T , then a full
optimization is performed after searching the interval (minh,maxh)
using the optimizer nlminb .
|
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" .
|
add
|
logical value, default is add=F . If add=T and display is not set
to "none" , then graphical output added to the existing plot, rather than
starting a new one.
|
poly.index
|
overall degree of locally-fitted polynomial, as used by sm.regression
|
display.rice
|
If this set to T (default is F ), a plot is produced of the curve
representing the modified Rice criterion for bandwidth selection.
See reference below for details.
|
...
|
Optional parameters passed to sm.regression .
|
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.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 parameter add and optional parameters (...{}
).
If display.rice=T
, 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.See Also
sm.regression
, sm.regression.autocor
Examples
# assume that Citrate and dogs are matrices
a <- sm.rm(y=Citrate, display.rice=T)
#
Time <- c(1,3,5,7,9,11,13)
gr1 <- as.matrix(dogs[id,2:8])
plot(c(1,13), c(3,6),xlab="time", ylab="potassium", type="n")
sm1 <- sm.rm(Time, gr1, display="se", add=T)