Re: [R] Find Crossover Points of Two Spline Functions

[Bert Gunter]

Use ?uniroot to do it numerically instead of polyroot()?

Cheers,
Bert
Bert Gunter

"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
   -- Clifford Stoll


On Mon, Sep 28, 2015 at 9:17 AM, Ben Bolker  wrote:
> Dario Strbenac  uni.sydney.edu.au> writes:
>
>>
>> Good day,
>>
>> I have two probability densities, each with a function determined
>> by splinefun(densityResult[['x']],
>> densityResult[['y']], "natural"), where densityResult is the
>> output of the density function in stats.
>> How can I determine all of the x values at which the densities cross ?
>>
>
>   My initial thought was this is non-trivial, because the two densities could
> cross (or nearly-but-not-quite cross) at an unlimited number of points.
> I thought it would essentially boils down to "how do I find all
> the roots of an arbitrary (continuous, smooth) function?
>
>   However, after thinking about it for a few more seconds I realize
> that at least the functions are piecewise cubic.  I still don't see
> a *convenient* way to do it ... if the knots were all coincident between
> the two densities (maybe you could constrain them to be so?) then you
> just have a difference of cubics within each segment, and you can use
> polyroot() to find the roots (and throw out any that are complex or
> don't fall within the segment).
>   If the knots are not coincident it's more of a pain but you should
> still be able to do it by considering overlapping segments ...
>
> __
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Re: [R] Find Crossover Points of Two Spline Functions

[Bert Gunter]

... (should have added)

However one might ask: Isn't this just a bit silly? The density()
function gives kernel density estimates (perhaps interpolated by
?approx -- see ?density) on as fine a grid as one likes, so why use
splines thereafter? And since these are density estimates -- i.e.
fitted approximations -- anyway, why search for "exact" solutions? Of
course I don't know the detailed context, but this whole question
sounds somewhat bogus.

Cheers,
Bert


Bert Gunter

"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
   -- Clifford Stoll


On Mon, Sep 28, 2015 at 9:36 AM, Bert Gunter  wrote:
> Use ?uniroot to do it numerically instead of polyroot()?
>
> Cheers,
> Bert
> Bert Gunter
>
> "Data is not information. Information is not knowledge. And knowledge
> is certainly not wisdom."
>-- Clifford Stoll
>
>
> On Mon, Sep 28, 2015 at 9:17 AM, Ben Bolker  wrote:
>> Dario Strbenac  uni.sydney.edu.au> writes:
>>
>>>
>>> Good day,
>>>
>>> I have two probability densities, each with a function determined
>>> by splinefun(densityResult[['x']],
>>> densityResult[['y']], "natural"), where densityResult is the
>>> output of the density function in stats.
>>> How can I determine all of the x values at which the densities cross ?
>>>
>>
>>   My initial thought was this is non-trivial, because the two densities could
>> cross (or nearly-but-not-quite cross) at an unlimited number of points.
>> I thought it would essentially boils down to "how do I find all
>> the roots of an arbitrary (continuous, smooth) function?
>>
>>   However, after thinking about it for a few more seconds I realize
>> that at least the functions are piecewise cubic.  I still don't see
>> a *convenient* way to do it ... if the knots were all coincident between
>> the two densities (maybe you could constrain them to be so?) then you
>> just have a difference of cubics within each segment, and you can use
>> polyroot() to find the roots (and throw out any that are complex or
>> don't fall within the segment).
>>   If the knots are not coincident it's more of a pain but you should
>> still be able to do it by considering overlapping segments ...
>>
>> __
>> 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.

__
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Re: [R] Find Crossover Points of Two Spline Functions

[Ben Bolker]

Dario Strbenac  uni.sydney.edu.au> writes:

> 
> Good day,
> 
> I have two probability densities, each with a function determined
> by splinefun(densityResult[['x']],
> densityResult[['y']], "natural"), where densityResult is the
> output of the density function in stats.
> How can I determine all of the x values at which the densities cross ?
> 

  My initial thought was this is non-trivial, because the two densities could
cross (or nearly-but-not-quite cross) at an unlimited number of points.
I thought it would essentially boils down to "how do I find all 
the roots of an arbitrary (continuous, smooth) function?

  However, after thinking about it for a few more seconds I realize
that at least the functions are piecewise cubic.  I still don't see
a *convenient* way to do it ... if the knots were all coincident between
the two densities (maybe you could constrain them to be so?) then you
just have a difference of cubics within each segment, and you can use
polyroot() to find the roots (and throw out any that are complex or
don't fall within the segment).
  If the knots are not coincident it's more of a pain but you should
still be able to do it by considering overlapping segments ...

__
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https://stat.ethz.ch/mailman/listinfo/r-help
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and provide commented, minimal, self-contained, reproducible code.

[R] Find Crossover Points of Two Spline Functions

[Dario Strbenac]

Good day,

I have two probability densities, each with a function determined by 
splinefun(densityResult[['x']], densityResult[['y']], "natural"), where 
densityResult is the output of the density function in stats. How can I 
determine all of the x values at which the densities cross ?

--
Dario Strbenac
PhD Student
University of Sydney
Camperdown NSW 2050
Australia

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Re: [R] Find Crossover Points of Two Spline Functions

[Ben Bolker]

Bert Gunter  gmail.com> writes:

> 
> Use ?uniroot to do it numerically instead of polyroot()?
> 
> Cheers,
> Bert
> Bert Gunter

  The problem with uniroot() is that we don't know how many intersections/
roots we might be looking for. With polyroot(), we know that there can
be at most 3 roots that lie within a particular cubic segment.

  (I'm not arguing that doing this is necessarily a good idea in
the larger context.)

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