Dear R-users, To obtain the percentage of deviance explained when fitting a gam model using the mgcv library is straightforward: summary(object.gam) $dev.expl or alternatively, using the deviance (deviance(object.gam)) of the null and the fitted models, and then using 1 minus the quotient of deviances. However, when a gamm (generalizad aditive mixed model) is fitted, the deviance is not displayed, and only the logLik of the underlying lme model can be derived (logLik(objetct.gamm$lme)), which is not enough to derive the percentage deviance explained because the logLik for the saturated model is not available. Any suggestions on how to obtain the deviance explained when a gamm is fitted when the typical default gauusian model is fitted? Or alternavely, are the R^2 derived from a gam model and a gamm model comparable? Thanks a lot in advance, Berta _________________________________________________________________ Descárgate ahora el nuevo Internet Explorer 8 y ten a tu alcance todos lo [[alternative HTML version deleted]]
Hi all, I have the same question for a GAMM with quasi-poisson errors. Does anyone know how to calculate the % deviance explained by a GAMM model or any other method evaluating the % contribution of the model in explaining the response variable (eg. number of birds in an area)? Thanks Nicole However, when a gamm (generalizad aditive mixed model) is fitted, the deviance is not displayed, and only the logLik of the underlying lme model can be derived (logLik(objetct.gamm$lme)), which is not enough to derive the percentage deviance explained because the logLik for the saturated model is not available. Any suggestions on how to obtain the deviance explained when a gamm is fitted when the typical default gauusian model is fitted? Or alternavely, are the R^2 derived from a gam model and a gamm model comparable? -- View this message in context: http://www.nabble.com/Deviance-explined-in-GAMM%2C-library-mgcv-tp23745983p23864721.html Sent from the R help mailing list archive at Nabble.com.
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