<p>Mikis' answer below... As guessed by Pr. Ripley, I was comparing apples and oranges indeed, and would have been better inspired looking at methods !</p><p>i Patrick <br /> <br /><a href="mailto:[EMAIL PROTECTED]" class="moz-txt-link-abbreviated">[EMAIL PROTECTED]</a> wrote: <br /><blockquote type="cite">Dear all and Mikis <br /> <br />I have the opportunity to compare fits with the 'classical' glm and gamlss and no smoother of any kind just the same model formula (both with the binomial family). I get exactly the same coefficients but very different residuals, gamlss giving residuals which are extremely close to 'normal' and glm very far... <br /> </blockquote>GAMLSS is calculating the (randomized) quantile residuals which if the distribution is correctly specified should be normal. <br />The nice think about the quantile residuals is that they can be applied to any distribution. <br />An additional complication arises for discrete distributions where also a randomization is taken place because of the discrete nature of the data. <br />See the rqres.plot() function for more details. <br />Another way of looking at the gamlss residuals is that they are the fitted z-scores for the individual observations. <br /> <br />The glm() function is using either deviance or Pearson residuals<br></br> <br /> <br />Mikis <blockquote type="cite">How can this be ? <br /> <br />Thanks in advance for any indication or threat... <br /> <br />Patrick <br /> <br /> <br /> <br /> <br /> <br />---------------------------------------------------------------- <br />This message was sent using IMP, the Internet Messaging Program. <br /> <br /> <br /> </blockquote> <br /> <br />Companies Act 2006 : <a href="http://www.londonmet.ac.uk/companyinfo"; class="moz-txt-link-freetext">http://www.londonmet.ac.uk/companyinfo</a> <br /> <br /> <br />
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