sp wrote:
Sincere thanks for both the replies.
0. I agree, I'm waiting for my copy of a regression book to arrive. Meanwhile,
I'm trying to read on google.
1. My bad, I'm using Gaussian noise.
2. I didn't have x^3 b/c that co-efficient happens to be zero in this fitting.
That's strange.
3. I used lines() b/c I wanted to superimpose the curve from regression atop my first plot of the original data points (x,y).
I'm not sure how to use plot(f, x1 = NA) after my first plot(). The examples I managed to find on google all use plot() followed by lines(). [In Matlab, I'd just say "hold" in between these calls.]
plot(f, x1=NA)
plot(f, x2=NA, add=TRUE)
Also, I'm forced to call win.graph() before my first plot() to see the first
plot. Is that normal?
no
4. I really could use some guidance on this part. I need to use rcs() to fit points in a high-dimensional space and I'm trying to understand and use it correctly.
keep reading
I started with testing it on just x,y dimensions so that I can visually evaluate the fitting. I tried y=x, y=x^2 etc, adding Gaussian noise each time (to the y).
I plot original x,y and x,y' where y' is calculated using the co-efficients
returned by rcs. I find that the regression curve differs from the actual
points by as high as 10^5 with 3 knots and roughly -10^5 with 4 knots as I make
y=x^2, y=x^3....
wait until you have studied regression
Frank
If this is NOT a good way to test fitting, could you pls tell me a better way?
Respectfully,
sp
--- On Tue, 12/23/08, Frank E Harrell Jr <f.harr...@vanderbilt.edu> wrote:
From: Frank E Harrell Jr <f.harr...@vanderbilt.edu>
Subject: Re: [R] newbie problem using Design.rcs
To: "David Winsemius" <dwinsem...@comcast.net>
Cc: to_rent_2...@yahoo.com, r-help@r-project.org
Date: Tuesday, December 23, 2008, 9:41 AM
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
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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