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

[Michael Grant]

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 <Jan.Verbesselt at biw.kuleuven.be>
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 //
> 
> 
> 
> 
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