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.
