# [R] Orthogonal polynomials used by R

Ashim Kapoor @@h|mk@poor @end|ng |rom gm@||@com
Thu Nov 28 06:46:45 CET 2019

```Dear Peter and John,

Many thanks for your prompt replies.

Here is what I was trying to do.  I was trying to build a statistical model
of a given time series using Box Jenkins methodology. The series has 93
data points. Before I analyse the ACF and PACF, I am required to de-trend
the series. The series seems to have an upward trend. I wanted to find out
what order polynomial should I fit the series
without overfitting.  For this I want to use orthogonal polynomials(I think
someone on the internet was talking about preventing overfitting by using
orthogonal polynomials) . This seems to me as a poor man's cross
validation.

So my plan is to keep increasing the degree of the orthogonal polynomials
till the coefficient of the last orthogonal polynomial becomes
insignificant.

Note : If I do NOT use orthogonal polynomials, I will overfit the data set
and I don't think that is a good way to detect the true order of the
polynomial.

Also now that I have detrended the series and built an ARIMA model of the
residuals, now I want to forecast. For this I need to use the original
polynomials and their coefficients.

I hope I was clear and that my methodology is ok.

I have another query here :-

Note : If I used cross-validation to determine the order of the polynomial,
I don't get a clear answer.

See here :-
library(boot)
mydf = data.frame(cbind(gdp,x))
d<-(c(
cv.glm(data = mydf,glm(gdp~x),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~poly(x,2)),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~poly(x,3)),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~poly(x,4)),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~poly(x,5)),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~poly(x,6)),K=10)\$delta))
print(d)
##  2.178574e+13 7.303031e+11 5.994783e+11 4.943586e+11 4.596648e+11
##  4.980159e+11

# Here it chooses 5. (but 4 and 5 are kind of similar).

d1 <- (c(
cv.glm(data = mydf,glm(gdp~1+x),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~1+x+x^2),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~1+x+x^2+x^3),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~1+x+x^2+x^3+x^4),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~1+x+x^2+x^3+x^4+x^5),K=10)\$delta,
cv.glm(data = mydf,glm(gdp~1+x+x^2+x^3+x^4+x^5+x^6),K=10)\$delta))

print(d1)
##  2.149647e+13 2.253999e+13 2.182175e+13 2.177170e+13 2.198675e+13
##  2.145754e+13

# here it chooses 1 or 6

Query : Why does it choose 1? Notice : Is this just round off noise / noise
due to sampling error created by Cross Validation when it creates the K
folds? Is this due to the ill conditioned model matrix?

Best Regards,
Ashim.

On Wed, Nov 27, 2019 at 10:41 PM Fox, John <jfox using mcmaster.ca> wrote:

> Dear Ashim,
>
> Orthogonal polynomials are used because they tend to produce more accurate
> numerical computations, not because their coefficients are interpretable,
> so I wonder why you're interested in the coefficients.
>
> The regressors produced are orthogonal to the constant regressor and are
> orthogonal to each other (and in fact are orthonormal), as it's simple to
> demonstrate:
>
> ------- snip -------
>
> > x <- 1:93
> > y <- 1 + x + x^2 + x^3 + x^4 + rnorm(93)
> > (m <- lm(y ~ poly(x, 4)))
>
> Call:
> lm(formula = y ~ poly(x, 4))
>
> Coefficients:
> (Intercept)  poly(x, 4)1  poly(x, 4)2  poly(x, 4)3  poly(x, 4)4
>    15574516    172715069     94769949     27683528      3429259
>
> > X <- model.matrix(m)
>   (Intercept) poly(x, 4)1 poly(x, 4)2 poly(x, 4)3 poly(x, 4)4
> 1           1  -0.1776843   0.2245083  -0.2572066  0.27935949
> 2           1  -0.1738216   0.2098665  -0.2236579  0.21862917
> 3           1  -0.1699589   0.1955464  -0.1919525  0.16390514
> 4           1  -0.1660962   0.1815482  -0.1620496  0.11487597
> 5           1  -0.1622335   0.1678717  -0.1339080  0.07123722
> 6           1  -0.1583708   0.1545171  -0.1074869  0.03269145
>
> > zapsmall(crossprod(X))# X'X
>             (Intercept) poly(x, 4)1 poly(x, 4)2 poly(x, 4)3 poly(x, 4)4
> (Intercept)          93           0           0           0           0
> poly(x, 4)1           0           1           0           0           0
> poly(x, 4)2           0           0           1           0           0
> poly(x, 4)3           0           0           0           1           0
> poly(x, 4)4           0           0           0           0           1
>
> ------- snip -------
>
> If for some not immediately obvious reason you're interested in the
> regression coefficients, why not just use a "raw" polynomial:
>
> ------- snip -------
>
> > (m1 <- lm(y ~ poly(x, 4, raw=TRUE)))
>
> Call:
> lm(formula = y ~ poly(x, 4, raw = TRUE))
>
> Coefficients:
>             (Intercept)  poly(x, 4, raw = TRUE)1  poly(x, 4, raw = TRUE)2
> poly(x, 4, raw = TRUE)3
>                  1.5640                   0.8985                   1.0037
>                  1.0000
> poly(x, 4, raw = TRUE)4
>                  1.0000
>
> ------- snip -------
>
> These coefficients are simply interpretable but the model matrix is more
> poorly conditioned:
>
> ------- snip -------
>
>   (Intercept) poly(x, 4, raw = TRUE)1 poly(x, 4, raw = TRUE)2 poly(x, 4,
> raw = TRUE)3
> 1           1                       1                       1
>          1
> 2           1                       2                       4
>          8
> 3           1                       3                       9
>         27
> 4           1                       4                      16
>         64
> 5           1                       5                      25
>        125
> 6           1                       6                      36
>        216
>   poly(x, 4, raw = TRUE)4
> 1                       1
> 2                      16
> 3                      81
> 4                     256
> 5                     625
> 6                    1296
> > round(cor(X1[, -1]), 2)
>                         poly(x, 4, raw = TRUE)1 poly(x, 4, raw = TRUE)2
> poly(x, 4, raw = TRUE)3
> poly(x, 4, raw = TRUE)1                    1.00                    0.97
>                 0.92
> poly(x, 4, raw = TRUE)2                    0.97                    1.00
>                 0.99
> poly(x, 4, raw = TRUE)3                    0.92                    0.99
>                 1.00
> poly(x, 4, raw = TRUE)4                    0.87                    0.96
>                 0.99
>                         poly(x, 4, raw = TRUE)4
> poly(x, 4, raw = TRUE)1                    0.87
> poly(x, 4, raw = TRUE)2                    0.96
> poly(x, 4, raw = TRUE)3                    0.99
> poly(x, 4, raw = TRUE)4                    1.00
>
> ------- snip -------
>
> The two parametrizations are equivalent, however, in that they represent
> the same regression surface, and so, e.g., produce the same fitted values:
>
> ------- snip -------
>
> > all.equal(fitted(m), fitted(m1))
>  TRUE
>
> ------- snip -------
>
> Because one is usually not interested in the individual coefficients of a
> polynomial there usually isn't a reason to prefer one parametrization to
> the other on the grounds of interpretability, so why do you need to
> interpret the regression equation?
>
> I hope this helps,
>  John
>
>   -----------------------------
>   John Fox, Professor Emeritus
>   McMaster University
>   Web: http::/socserv.mcmaster.ca/jfox
>
> > On Nov 27, 2019, at 10:17 AM, Ashim Kapoor <ashimkapoor using gmail.com>
> wrote:
> >
> > Dear Petr,
> >
> > Many thanks for the quick response.
> >
> > I also read this:-
> > https://en.wikipedia.org/wiki/Discrete_orthogonal_polynomials
> >
> > Also I read  in ?poly:-
> >     The orthogonal polynomial is summarized by the coefficients, which
> >     can be used to evaluate it via the three-term recursion given in
> >     Kennedy & Gentle (1980, pp. 343-4), and used in the ‘predict’ part
> >     of the code.
> >
> >
> > Out of curiosity, what is the name of the discrete orthogonal polynomial
> > used by R ?
> > What discrete measure is it orthogonal with respect to ?
> >
> > Many thanks,
> > Ashim
> >
> >
> >
> >
> > On Wed, Nov 27, 2019 at 6:11 PM PIKAL Petr <petr.pikal using precheza.cz>
> wrote:
> >
> >> You could get answer quickly by searching net.
> >>
> >>
> >>
> https://stackoverflow.com/questions/39031172/how-poly-generates-orthogonal-p
> >> olynomials-how-to-understand-the-coefs-ret/39051154#39051154
> >> <
> https://stackoverflow.com/questions/39031172/how-poly-generates-orthogonal-polynomials-how-to-understand-the-coefs-ret/39051154#39051154
> >
> >>
> >> Cheers
> >> Petr
> >>
> >>> -----Original Message-----
> >>> From: R-help <r-help-bounces using r-project.org> On Behalf Of Ashim Kapoor
> >>> Sent: Wednesday, November 27, 2019 12:55 PM
> >>> To: R Help <r-help using r-project.org>
> >>> Subject: [R] Orthogonal polynomials used by R
> >>>
> >>> Dear All,
> >>>
> >>> I have created a time trend by doing x<-1:93 because I have a time
> series
> >>> with 93 data points. Next I did :-
> >>>
> >>> y = lm(series ~ poly(x,4))\$residuals
> >>>
> >>> to detrend series.
> >>>
> >>> I choose this 4 as the order of my polynomial using cross validation/
> >>> checking the absence of trend in the residuals so I think I have not
> >> overfit
> >>> this series.
> >>>
> >>> I wish to document the formula of poly(x,4). I am not able to find it
> in
> >> ?poly
> >>>
> >>> Can someone please tell me what the formula for the orthogonal
> >>> polynomial used by R is ?
> >>>
> >>> Thank you,
> >>> Ashim
> >>>
> >>>      [[alternative HTML version deleted]]
> >>>
> >>> ______________________________________________
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> >>> guide.html
> >>> and provide commented, minimal, self-contained, reproducible code.
> >>
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
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