Dear package mgcv users,
I am using package mgcv to describe presence of a migratory bird species as
a function of several variables, including year, day number (i.e.
day-of-the-year), duration of survey, latitude and longitude. Thus, the
"global model" is:
global_model<-gam(present ~ as.factor(year) + s(dayno, k=5) + s(duration,
k=5) + s(x, k=5) + s(y, k=5), family = binomial(link = logit), data =
presence, gamma =1.4)
The model works fine, suggesting that all the variables have strong,
non-linear, influences on the probability of detecting the species. My main
interest is in the effect "dayno" has on presence, given the inclusion of
the other explanatory variables. Thus, I would like to extract the values of
the partial prediction of "dayno" and its associated 2 standard errors above
and below the main effect, as shown by the code "plot(global_model)".
I have tried to extract the values by using
"fitted.values(global_model,dayno)", but when plotted, the figure doesn't
look like the partial prediction for "dayno". Instead, it gives me a very
scattered figure ("shotgun effect").
Has anyone tried to extract the partial predictions? If so, please could you
advise me how to do this?
Thanks,
Staffan
P.S.. For comparison, please have a look at Simon Woods paper in R News,
1(2):20-25, June 2001, especially the figures showing the partial
predictions of Mackerel egg densities. Using those graphs as an example, I
would like to extract the partial predictions for e.g. "s(b.depth)", given
the inclusion of the other variables.
--
Staffan Roos, PhD
Research Ecologist
BTO Scotland
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