Dear Alejandra
in case you want to move on before Simon replies see inline
On 12/01/2018 22:50, Alejandra Martínez Blancas wrote:
Thanks Simon, by cloning a smooth construct do you mean copying and
modifying the smooth constructor code?
That is what I understand him to mean yes. (I believe it is clon in
Spanish if that helps).
Could you pleas elaborate on
your answer? Which is the Predict.matrix method?
2018-01-12 3:20 GMT-06:00 Simon Wood <simon.w...@bath.edu>:
There probably is a way, but it involves some programming. You would need to
clone a smooth constructor (e.g. for the "cr" class), and then modify it to
add a linear constraint matrix C to the returned smooth object. If b are the
smooth coefficients then C should be the matrix such that s(0) = Cb (you
can get this from the Predict.matrix method for the class). Then the
constraint Cb=0 will be applied during basis setup, and is equivalent to
s(0)=0.
Now you can use your cloned class in a tensor product smooth, using the 'ti'
constructor. Suppose your cloned smooth class is called "foo", then
ti(x,z,bs="foo",mc=c(0,1))
will create a smooth for which s(x,0)=0. Your requirement that s(x,0)=k is
then taken care of by the model intercept.
If you want to try something similar with the full nested structure it's
more complicated still. Then I think you would need something like
s(x,by=as.numeric(z!=0)) + s(z) + ti(x,z,bs=c("cr","foo"))
Simon
On 11/01/18 22:33, Alejandra Martínez Blancas wrote:
I am fitting a model in which the response variable y is a function of
two independent, quantitative variables x1 and x2; thus: y = f(x1,
x2). For reasons I do not believe to be important for the purpose of
this post, I find it desirable to find f by means of GAM; also, I
require principal effects and interactions to be specified separately,
so I am using using te and ti tensors. Thus, I am using the following
command:
f = gam(y ~ te(x1) + te(x2) + ti(x1, x2))
This results in a model that corresponds to one of the hypotheses I am
testing. Nevertheless, another hypothesis requires that, when one of
the independent variables (say x2) is zero, the value of y is
unaffected by the other variable (in this example x1). In other words
f(x1, 0) = k for every value of x1, where k is a constant to be
estimated. For x2 values other than zero I would like to let GAM
choose the appropriate function relating x1 and y. Is there a way to
specify such model in mgcv?
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--
Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK
+44 (0)117 33 18273 http://www.maths.bris.ac.uk/~sw15190
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______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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--
Michael
http://www.dewey.myzen.co.uk/home.html
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