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...score = 4.0288 Scale est. = 3.8082 n = 400 ## note that the preceding fit is the same as.... b1<-gam(y ~ s(x2,by=as.numeric(fac==1))+s(x2,by=as.numeric(fac==2))+ s(x2,by=as.numeric(fac==3))+s(x0)-1,data=dat) ## ... the `-1' is because the intercept is confounded with the ## *uncentred* smooths here. plot(b1,pages=1) summary(b1) Family: gaussian Link function: identity Formula: y ~ s(x2, by = as.numeric(fac == 1)) + s(x2, by = as.numeric(fac == 2)) + s(x2, by = as.numeric(fac == 3)) + s(x0) - 1 Approximate significance of smooth terms: edf Ref....

Dear All, The help for prcomp, under "Value" says: sdev: the standard deviation of the principal components (i.e., the eigenvalues of the cov matrix, though the calculation is actually done with the singular values of the data matrix). The way I read it, it implies that the sdev are the eigenvalues, but I think that sdev is actually the square root of the

...score = 4.0288 Scale est. = 3.8082 n = 400 ## note that the preceding fit is the same as.... b1<-gam(y ~ s(x2,by=as.numeric(fac==1))+s(x2,by=as.numeric(fac==2))+ s(x2,by=as.numeric(fac==3))+s(x0)-1,data=dat) ## ... the `-1' is because the intercept is confounded with the ## *uncentred* smooths here. plot(b1,pages=1) summary(b1) Family: gaussian Link function: identity Formula: y ~ s(x2, by = as.numeric(fac == 1)) + s(x2, by = as.numeric(fac == 2)) + s(x2, by = as.numeric(fac == 3)) + s(x0) - 1 Approximate significance of smooth terms: edf Ref....

Has anyone programmed condition indexes in R? I know that there is a function for variance inflation factors available in the car package; however, Belsley (1991) Conditioning Diagnostics (Wiley) notes that there are several weaknesses of VIFs: e.g. 1) High VIFs are sufficient but not necessary conditions for collinearity 2) VIFs don't diagnose the number of collinearities and 3) No one has