That is what I understand him to mean yes. (I believe it is clon in

Spanish if that helps).

> your answer? Which is the Predict.matrix method?

>

> 2018-01-12 3:20 GMT-06:00 Simon Wood <

[hidden email]>:

>> 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?

>>>

>>> ______________________________________________

>>>

[hidden email] 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.

>>

>>

>>

>> --

>> Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK

>> +44 (0)117 33 18273

http://www.maths.bris.ac.uk/~sw15190>>

>> ______________________________________________

>>

[hidden email] 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.

>

> ______________________________________________

>

[hidden email] 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.

>