Thanks David for getting back to me. I think I have followed your answer, thank you, and I get that when one specifies the theta value, all the ft3$phis are now constant for each lambda.
Now I wonder if there is any value of ever specifying " negative.binomial(theta) " as I did below with ft3 (cf the ?glm1path helpfile) to improve the residuals, when using the LASSO? I guess I always thought the LASSO was a more robust way to select models but it seems the residuals of ft2 suggest otherwise. These questions are motivated for some over dispersed seal-fish data for a student in Sydney (as we've discussed off list) but I guess these questions are more of a theoretical nature. I over came my social phobia of posting on a list instead of hassling you privately(!), maybe someone else can value from this discussion too :) Thanks once again, Jo On Mon, Dec 3, 2018 at 12:10 AM David Warton <david.war...@unsw.edu.au> wrote: > Hi Jo, > > Thanks for the e-mail, always good to see statistical modelling questions > on this list! > > > > In the mvabund package, you can fit trait models using different methods > of estimation, method=”manyglm” will fit a GLM, “glm1path” will fit a GLM > with a LASSO penalty (chosen using BIC by default but there are other > options). The way we coded LASSO negative binomial regression was to > update estimates of the overdispersion parameter as the slope parameters > update. Because the LASSO fit gives different slope parameters, it will > also have a different overdispersion parameter. It probably has a larger > overdispersion parameter, because the LASSO pushes slope parameters away > from the best (in-sample) fit hence there is more unexplained variation in > the LASSO model. > > > > All the best > > David > > > > Professor David Warton > > School of Mathematics and Statistics, Evolution & Ecology Research Centre, > Centre for Ecosystem Science > > UNSW Sydney > > NSW 2052 AUSTRALIA > > phone +61(2) 9385 7031 > > fax +61(2) 9385 7123 > > > > http://www.eco-stats.unsw.edu.au > > > > > > > > *From:* Joanne Potts <joa...@theanalyticaledge.com> > *Sent:* Friday, 30 November 2018 1:51 AM > *To:* r-sig-ecology@r-project.org > *Cc:* David Warton <david.war...@unsw.edu.au> > *Subject:* mvabund: difference between 'glm1path' and 'manyglm' > > > > > > Hi David and list, > > > > Can someone please help me understand why, when changing the > 'method=manyglm' argument to 'method=glm1path' under default settings > (negative binomial) the estimates of theta change in the 'trait.glm' > function? > > > > I have provided example code below us the antTraits data set. And you > should see the plots for ft and ft3 are similar, yet ft2 is quite > different, so I think I am missing something (no doubt, probably very > obvious!). > > > > Advice appreciated, thank you. > > > > Jo > > > > > > > > data(antTraits) > > > > > ft=traitglm(antTraits$abund,antTraits$env,antTraits$traits,method="manyglm") > > ft$phi > > ft$theta > > qqnorm(residuals(ft)); abline(c(0,1),col="red") > > > > > ft2=traitglm(antTraits$abund,antTraits$env,antTraits$traits,method="glm1path") > > mean(ft2$phis) > > qqnorm(residuals(ft2)); abline(c(0,1),col="red") > > > > > > ft3=traitglm(antTraits$abund,antTraits$env,antTraits$traits,method="glm1path", > negative.binomial(theta=1.641763)) > > 1/mean(ft3$phis) > > qqnorm(residuals(ft3)); abline(c(0,1),col="red") > > > > > > -- > > > > Kind regards, > > > > Joanne Potts > > > > > > ___________________ > > Statistical Consultant > > theanalyticaledge.com > -- Kind regards, Joanne Potts ___________________ Statistical Consultant theanalyticaledge.com [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology