On Aug 31, 2009, at 7:19 PM, Josef Fruehwald wrote:
Hi all,
I'm using the ssanova function from the gss package to fit smoothing
spline
anovas, and am running into some difficulty.
For my data, I have measurements at 2 milisecond intervals for every
observation. Every observation does not have the same duration, so I
have
scaled the times for each observation to a scale between 0 and 1. I
would
like to smooth over time, and the following works:
ssanova(Measurement ~ ScaleTime, data = data)
I would also like to see how the variable duration affects the
curve, so I
have another column in the dataframe which contains the log
duration. I did
it like so:
Durations <- data.frame(LogDuration = log(tapply(data$Time, data
$Token,
max)), Token = levels(data$Token)
That looks wrong. The results of tapply will not in general be a
single number, so LogDuration could be a rather weird list of things.
Have you run summary() on it?
data <- merge(data, Durations, by = "Token")
But maybe I am not really understanding your genius.
Now every measurement point for every observation also has the
log(duration)
of the entire observation associated with it.
I would assume that the following is how I should specify my formula:
ssanova(Measurement ~ ScaleTime * LogDuration, data = data)
I wonder if log time (once you confirm that the variable is what you
want it to be) ought to be entered as an offset?
but I get the following error:
Error in if (!((2 * order > dm) & (dm >= 1))) { :
missing value where TRUE/FALSE needed
I get the same error if I try
ssanova(Measurement ~ LogDuration, data = data)
Any suggestions as to how I should approach this problem? I know
that if I
break duration into some kind of factor, I can successfully fit the
model.
However, I would like to assume that there is a continuous
transformation of
the curve shape as duration increases or decreases.
Thanks!
Joe
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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