The plots look reasonable to me. The plot of residuals against linear predictor always looks scary when many of the fitted values are very close to zero, so I tend to look at residuals against sqrt(fitted) in such cases. I don't think that the presence of the zero curve is a reason to reject the model --- it's easy to produce such plots by fitting a completely correct model to simulated count data.
best, Simon On 08/06/11 15:50, Samuel Turgeon wrote:
Dear list, i'm checking the residuals plots of a gam model after a processus of model selection. I found the "best" model, all my terms are significant, the r-square and the deviance explained are good, but I have strange residuals plots: http://dl.dropbox.com/u/1169100/gam.check.png http://dl.dropbox.com/u/1169100/residuals_vs_fitted.png The curve is caused by the zeroes in my data. I've also plotted each explanatory variables included in the model against residuals and everything looks fine. Is this curve does not allow me to accept this model? Does the use of an other family (eg negbin) would be the solution for fixing this problem? Currently I use the poisson family (and quasipoisson). I have a lot of 0 in my response variable, almost 65 %.... Should I use a specific function that allows me to use zero-inflated data?? Kind regards, Sam [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
-- Simon Wood, Mathematical Science, University of Bath BA2 7AY UK +44 (0)1225 386603 http://people.bath.ac.uk/sw283 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
