In addition to David's excellent response, I'll add that your problems
seem to be statistical and not programming ones. I recommend that you
spend a significant amount of time with a good regression text or course
before using the software. Also, with Design you can find out the
algebraic form of the fit:
f <- ols(y ~ rcs(x,3), data=mydata)
Function(f)
Frank
David Winsemius wrote:
On Dec 22, 2008, at 11:38 PM, sp wrote:
Hi,
I read data from a file. I'm trying to understand how to use
Design.rcs by using simple test data first. I use 1000 integer values
(1,...,1000) for x (the predictor) with some noise (x+.02*x) and I set
the response variable y=x. Then, I try rcs and ols as follows:
Not sure what sort of noise that is.
m = ( sqrt(y1) ~ ( rcs(x1,3) ) ); #I tried without sqrt also
f = ols(m, data=data_train.df);
print(f);
[I plot original x1,y1 vectors and the regression as in
y <- coef2[1] + coef2[2]*x1 + coef2[3]*x1*x1]
That does not look as though it would capture the structure of a
restricted **cubic** spline. The usual method in Design for plotting a
model prediction would be:
plot(f, x1 = NA)
But this gives me a VERY bad fit:
"
Can you give some hint why you consider this to be a "VERY bad fit"? It
appears a rather good fit to me, despite the test case apparently not
being construct with any curvature which is what the rcs modeling
strategy should be detecting.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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