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

From: Jan Verbesselt <Jan.Verbesselt_at_biw.kuleuven.be>
Date: Sat 17 Dec 2005 - 23:40:52 EST


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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