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

[Jan Verbesselt]

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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