R help - Jan 2008 - Specify a correct formula in R for Piecewise Linear Functions?

Dear all,
  I have two variables, y and x. It seems that the relationship between them
is Piecewise Linear Functions. The cutpoint is 20. That is, when x<20, there
is a linear relationship between y and x; while x>=20, there is another
different linear relationship between them.
How can i specify their relationships in R correctly?
# glm(y~I(x<20)+I(x>=20),family = binomial, data = point)  something like
this?
 Thanks a lot.

-- 
With Kind Regards,

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[***********************************************************************]
Zhi Jie,Zhang ,PHD
Tel:+86-21-54237149
Dept. of Epidemiology,School of Public Health,Fudan University
Address:No. 138 Yi Xue Yuan Road,Shanghai,China
Postcode:200032
Email:epistat@gmail.com
Website: www.statABC.com
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	[[alternative HTML version deleted]]

On Tue, 1 Jan 2008, zhijie zhang wrote:
> Dear all,
>  I have two variables, y and x. It seems that the relationship between them
> is Piecewise Linear Functions. The cutpoint is 20. That is, when x<20,
there
> is a linear relationship between y and x; while x>=20, there is another
> different linear relationship between them.
> How can i specify their relationships in R correctly?
> # glm(y~I(x<20)+I(x>=20),family = binomial, data = point)  something
like
> this?
Try this:
> library(splines)
> fit <- glm( y ~ bs( x, deg=1, knots=20 ), family=binomial)
HTH,

Chuck

> Thanks a lot.
>
> -- 
> With Kind Regards,
>
> oooO:::::::::
> (..):::::::::
> :\.(:::Oooo::
> ::\_)::(..)::
> :::::::)./:::
> ::::::(_/::::
> :::::::::::::
> [***********************************************************************]
> Zhi Jie,Zhang ,PHD
> Tel:+86-21-54237149
> Dept. of Epidemiology,School of Public Health,Fudan University
> Address:No. 138 Yi Xue Yuan Road,Shanghai,China
> Postcode:200032
> Email:epistat at gmail.com
> Website: www.statABC.com
> [***********************************************************************]
> oooO:::::::::
> (..):::::::::
> :\.(:::Oooo::
> ::\_)::(..)::
> :::::::)./:::
> ::::::(_/::::
> :::::::::::::
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> 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.
>
Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901

On Tue, 1 Jan 2008, Charles C. Berry wrote:
> On Tue, 1 Jan 2008, zhijie zhang wrote:
>
>> Dear all,
>>  I have two variables, y and x. It seems that the relationship between
them
>> is Piecewise Linear Functions. The cutpoint is 20. That is, when
x<20, there
>> is a linear relationship between y and x; while x>=20, there is
another
>> different linear relationship between them.
>> How can i specify their relationships in R correctly?
>> # glm(y~I(x<20)+I(x>=20),family = binomial, data = point) 
something like
>> this?
>
> Try this:
>
>> library(splines)
>> fit <- glm( y ~ bs( x, deg=1, knots=20 ), family=binomial)
>
In the linear case I would actually argue that there is a benefit from 
constructing the spline basis by hand, so that you know what the 
coefficients mean. (For quadratic and higher order splines I agree that 
pre-existing code for the B-spline basis makes a lot more sense).

For example, in
   fit <- glm( y ~ pmax(x,20)+pmin(x,20), family=binomial)
the coefficients are the slope when is < 20 and the slope when x>20.

 	-thomas


Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle

Try:

glm( y ~ x + I( (x-20)*(x>20) ), ...

This will give you a piecewise linear function with a change point at
x=20 and the 2 lines meeting at x=20.  The intercept and slope on x will
represent the line for x <= 20 and the slope on the I(...) term will
represent the difference in slope between the previous line and the new
slope for x>=20 (so the test on this term tests the null that a single
line fits as well as the piecewise linear).

Hope this helps,

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
(801) 408-8111
 
 
> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of zhijie zhang
> Sent: Tuesday, January 01, 2008 8:25 AM
> To: R-help at stat.math.ethz.ch
> Subject: [R] Specify a correct formula in R for Piecewise 
> Linear Functions?
> 
> Dear all,
>   I have two variables, y and x. It seems that the 
> relationship between them is Piecewise Linear Functions. The 
> cutpoint is 20. That is, when x<20, there is a linear 
> relationship between y and x; while x>=20, there is another 
> different linear relationship between them.
> How can i specify their relationships in R correctly?
> # glm(y~I(x<20)+I(x>=20),family = binomial, data = point)  
> something like this?
>  Thanks a lot.
> 
> --
> With Kind Regards,
> 
> oooO:::::::::
> (..):::::::::
> :\.(:::Oooo::
> ::\_)::(..)::
> :::::::)./:::
> ::::::(_/::::
> :::::::::::::
> [*************************************************************
> **********]
> Zhi Jie,Zhang ,PHD
> Tel:+86-21-54237149
> Dept. of Epidemiology,School of Public Health,Fudan 
> University Address:No. 138 Yi Xue Yuan Road,Shanghai,China
> Postcode:200032
> Email:epistat at gmail.com
> Website: www.statABC.com
> [*************************************************************
> **********]
> oooO:::::::::
> (..):::::::::
> :\.(:::Oooo::
> ::\_)::(..)::
> :::::::)./:::
> ::::::(_/::::
> :::::::::::::
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at r-project.org mailing list
> 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.
>