newbie problem using Design.rcs
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 at 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.