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.
--
David Winsemius
Linear Regression Model
ols(formula = m, data = data_train.df)
n Model L.R. d.f. R2 Sigma
1000 4573 2 0.9897 0.76
Residuals:
Min 1Q Median 3Q Max
-4.850930 -0.414008 -0.009648 0.418537 3.212079
Coefficients:
Value Std. Error t Pr(>|t|)
Intercept 5.90958 0.0672612 87.86 0
x1 0.03679 0.0002259 162.88 0
x1' -0.01529 0.0002800 -54.60 0
Residual standard error: 0.76 on 997 degrees of freedom
Adjusted R-Squared: 0.9897
"
I appreciate any and all help!
Sincerely,
sp
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
