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

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

 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 // Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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## Re: diagnostic functions to assess fitted ols() model: Confidence is too narrow?!

 Jan Verbesselt 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 Confidence intervals for means are not supposed to cover the data points.  This interval shrinks to zero as the sample size goes to infinity.  Confidence intervals that are 'individual' should cover the majority of data points. You can see the case study on ols in my book for examples of diagnostics.  See biostat.mc.vanderbilt.edu/rms Frank Harrell > > # -->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 // > > > > > Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm> > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html> -- Frank E Harrell Jr   Professor and Chair           School of Medicine                       Department of Biostatistics   Vanderbilt University ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Frank Harrell Department of Biostatistics, Vanderbilt University
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## Re: diagnostic functions to assess fitted ols() model: Confidence is too narrow?!

 In reply to this post by Jan Verbesselt 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 <[hidden email]> 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 // > > > > > Disclaimer: > http://www.kuleuven.be/cwis/email_disclaimer.htm> > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html> ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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