That said, the gist of the OP's outline is correct, and the main reason to look elsewhere is to get more thorough advice on what statistical concerns should be addressed than would be on topic here.
One comment: reviewing plots of differences versus various independent variables for systematic biases is a task R is particularly well suited for, but discovering which plots highlight issues with your model or data takes familiarity with your data (explore) and with theory (which you learn elsewhere) and with R (which we can help with if you have more specific questions). On January 8, 2019 10:50:14 AM PST, Bert Gunter <bgunter.4...@gmail.com> wrote: >This list is (mostly) about R programming. Your query is (mostly) about >statistics. So you should post on a statistics site like >stats.stackexchange.com >not here; I am pretty sure you'll receive lots of answers there. > >Cheers, >Bert > > >Bert Gunter > >"The trouble with having an open mind is that people keep coming along >and >sticking things into it." >-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > >On Tue, Jan 8, 2019 at 10:18 AM Maria Eugenia Utgés ><mariaeugen...@gmail.com> >wrote: > >> Hi R-list, >> We have constructed a hurdle model some time ago. >> Now we were able to gather new data in the same city (38 new sites), >and >> want to do an external validation to see if the model still performs >ok. >> All the books and lectures I have read say its the best validation >option >> but... >> I have made a (simple) search, but it seems that as having new data >for a >> model is rare, have not found anything with the depth enough so as to >> reproduce it/adapt it to hurdle models. >> >> I have predicted the probability for non-zero counts >> nonzero <- 1 - predict(final, newdata = datosnuevos, type = "prob")[, >1] >> >> and the predicted mean from the count component >> countmean <- predict(final, newdata = datosnuevos, type = "count") >> >> I understand that "newdata" is taking into account the new values for >the >> independent variables (environmental variables), is it? >> >> So, I have to compare the predicted values of y (calculated with the >new >> values of the environmental variables) with the new observed values. >> >> That would be using the model (constructed with the old values), >having as >> input the new variables, and having as output a "new" prediction, to >be >> contrasted with the "new" observed y. >> >> These comparison would be by means of AUC, correct classification, >and/or >> what other options? Results of the external validation would just be >a % of >> correct predicted values? plots? >> >> Need some guidance, sorry if the explanation was "basic" but needed >to >> write it in my own words so as not to miss any detail. >> >> Thank you very much in advance, >> >> María Eugenia Utgés >> >> CeNDIE-ANLIS >> Buenos Aires >> Argentina >> a >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> 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. >> > > [[alternative HTML version deleted]] > >______________________________________________ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >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. -- Sent from my phone. Please excuse my brevity. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
