Re: [R] diagnostic functions to assess fitted ols() model: Confidence is too narrow?!

From: Michael Grant <mwgrant2001_at_yahoo.com>
Date: Sun 18 Dec 2005 - 04:35:57 EST

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

> 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 //
>
>
>
>
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R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sun Dec 18 04:48:59 2005

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