[R] diagnostic functions to assess fitted ols() model: Confidence is too narrow?!
Jan Verbesselt
Jan.Verbesselt at biw.kuleuven.be
Sat Dec 17 13:40:52 CET 2005
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
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