Re: [R] setting constraints on gam

[Michael Dewey]

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 :

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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--
Michael
http://www.dewey.myzen.co.uk/home.html

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Re: [R] setting constraints on gam

[Alejandra Martínez Blancas]

Thanks Simon, by cloning a smooth construct do you mean copying and
modifying the smooth constructor code? Could you pleas elaborate on
your answer? Which is the Predict.matrix method?

2018-01-12 3:20 GMT-06:00 Simon Wood :
> 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?
>>
>> __
>> 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.
>
>
>
> --
> Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK
> +44 (0)117 33 18273 http://www.maths.bris.ac.uk/~sw15190
>
> __
> 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.

__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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Re: [R] setting constraints on gam

[Simon Wood]

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?

__
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.



--
Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK
+44 (0)117 33 18273 http://www.maths.bris.ac.uk/~sw15190

__
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
and provide commented, minimal, self-contained, reproducible code.

[R] setting constraints on gam

[Alejandra Martínez Blancas]

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?

__
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.