First let me admit that I am no statistician... rather, an ecologist with
just enough statistical knowledge to be dangerous.
I've got a dataset with percent ground cover values for species and other
entities. The data are left censored at zero, in that percent ground cover
cannot be negative. (My data rarely reach 100% cover so I haven't bothered
with adding a right censoring at 100). I've done some previous analyses
using survival analysis methods to create a predictive model for an entity
of particular interest... library("survival"); survreg(Surv(Y) ~ X).
However, I know my data do not really match linear modeling and would like
to work with some alternate methods, one of which is GAM. I noticed that
Yee and Mitchell (1991, p.589) stated that GAM is appropriate for "certain
types of survival data". How do I implement a survival data model in GAM
with R? I've searched both R help and the R site search, but not found
anything relevant.
Would it be as simple as library("survival");
library("mgcv"); gam(Surv(Y) ~
X) ???
While I have your attention, I have a related second question. I'd like to
model one entity (percent ground cover) as a function of another (also
percent ground cover). Is there any way to deal with a censored predictor
variable as well as the censored response?
Citation: Yee, T. W. & N. D. Mitchell. 1991. Generalized additive models
in plant ecology. Journal of Vegetation Science 2: 587-602.
Thanks,
-Eric Peterson
Vegetation Ecologist
Nevada Natural Heritage Program
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