Jan, It sounds like you are interested in the prediction interval (actually band). Take a look at rather nice exposition in Chapter 9 (pdf) of Helsel and Hirsch. It can be downloaded at the following USGS page:
http://pubs.usgs.gov/twri/twri4a3/ Regards, Michael Grant --- Jan Verbesselt <[EMAIL PROTECTED]> wrote: > Dear all, > > When fitting an "ols.model", the confidence interval > at 95% doesn't cover > the plotted data points because it is very narrow. > > Does this mean that the model is 'overfitted' or is > there a specific amount > of serial correlation in the residuals? > > Which R functions can be used to evaluate > (diagnostics) major model > assumptions (residuals, independence, variance) when > fitting ols models in > the Design package? > > Regards, > Jan > > # -->OLS regression > library(Design) > ols.1 <- ols(Y~rcs(X,3), data=DATA, x=T, y=T) > summary.lm(ols.1) # --> non-linearity is > significant > anova(ols.1) > > d <- datadist(Y,X) > options(datadist="d") > plot(ols.1) > #plot(ols.1, conf.int=.80, > conf.type=c('individual')) > points(X,Y) > scat1d(X, tfrac=.2) > > When plotting this confidence interval looks normal: > > #plot(ols.1, conf.int=.80, > conf.type=c('individual')) > > Workstation Windows XP > // R version 2.2 // > > > > > Disclaimer: > http://www.kuleuven.be/cwis/email_disclaimer.htm > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
