Re: [R] ordinary polynomial coefficients from orthogonal polynomials?

From: Frank E Harrell Jr <f.harrell_at_vanderbilt.edu>
Date: Tue 14 Jun 2005 - 23:46:50 EST

Prof Brian Ripley wrote:

> On Tue, 14 Jun 2005, James Salsman wrote:
> 
> 

>>How can ordinary polynomial coefficients be calculated
>>from an orthogonal polynomial fit?
> 
> 
> Why would you want to do that?  predict() is perfectly happy with an
> orthogonal polynomial fit and the `ordinary polynomial coefficients' are 
> rather badly determined in your example since the design matrix has a very 
> high condition number.

Brian - I don't fully see the relevance of the high condition number nowadays unless the predictor has a really bad origin. Orthogonal polynomials are a mess for most people to deal with.

Frank

> 
> 

>>I'm trying to do something like find a,b,c,d from
>> lm(billions ~ a+b*decade+c*decade^2+d*decade^3)
>>but that gives: "Error in eval(expr, envir, enclos) :
>>Object "a" not found"
> 
> 
> You could use
> 
> lm(billions ~ decade + I(decade^2) + I(decade^3))
> 
> except that will be numerically inaccurate, since
> 
> 

>>m <- model.matrix(~ decade + I(decade^2) + I(decade^3))
>>kappa(m)
> 
> [1] 3.506454e+16
> 
> 
> 
> 

>>>decade <- c(1950, 1960, 1970, 1980, 1990)
>>>billions <- c(3.5, 5, 7.5, 13, 40)
>>># source: http://www.ipcc.ch/present/graphics/2001syr/large/08.17.jpg
>>>
>>>pm <- lm(billions ~ poly(decade, 3))
>>>
>>>plot(decade, billions, xlim=c(1950,2050), ylim=c(0,1000),
>>
>>main="average yearly inflation-adjusted dollar cost of extreme weather
>>events worldwide")
>>
>>>curve(predict(pm, data.frame(decade=x)), add=TRUE)
>>># output: http://www.bovik.org/storms.gif
>>>
>>>summary(pm)
>>
>>Call:
>>lm(formula = billions ~ poly(decade, 3))
>>
>>Residuals:
>> 1 2 3 4 5
>> 0.2357 -0.9429 1.4143 -0.9429 0.2357
>>
>>Coefficients:
>> Estimate Std. Error t value Pr(>|t|)
>>(Intercept) 13.800 0.882 15.647 0.0406 *
>>poly(decade, 3)1 25.614 1.972 12.988 0.0489 *
>>poly(decade, 3)2 14.432 1.972 7.318 0.0865 .
>>poly(decade, 3)3 6.483 1.972 3.287 0.1880
>>---
>>Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
>>
>>Residual standard error: 1.972 on 1 degrees of freedom
>>Multiple R-Squared: 0.9957, Adjusted R-squared: 0.9829
>>F-statistic: 77.68 on 3 and 1 DF, p-value: 0.08317
>>
>>
>>>pm
>>
>>Call:
>>lm(formula = billions ~ poly(decade, 3))
>>
>>Coefficients:
>> (Intercept) poly(decade, 3)1 poly(decade, 3)2 poly(decade, 3)3
>> 13.800 25.614 14.432 6.483
>>
>>______________________________________________
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>>
>
>
-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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Received on Tue Jun 14 23:48:45 2005

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