[R] How to formulate quadratic function with interaction terms for the PLS fitting model?
Bert Gunter
bgunter.4567 at gmail.com
Thu Jul 13 16:43:56 CEST 2017
Below.
-- Bert
Bert Gunter
On Thu, Jul 13, 2017 at 3:07 AM, Luigi Biagini <luigi.biagini at gmail.com> wrote:
> I have two ideas about it.
>
> 1-
> i) Entering variables in quadratic form is done with the command I
> (variable ^ 2) -
> plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation =
> "LOO"
> You could also use a new variable NIR_sq <- (NIR) ^ 2
>
> ii) To insert a square variable, use syntax I (x ^ 2) - it is very
> important to insert I before the parentheses.
True, but better I believe: see ?poly.
e.g. poly(cbind(x1,x2,x3), degree = 2, raw = TRUE) is a full quadratic
polynomial in x1,x2,x3 .
>
> iii) If you want to make the interaction between x and x ^ 2 use the
> command ":" -> x: I(x ^ 2)
>
> iv) For multiple interactions between x and x ^ 2 use the command "*" -> x
> *I (x ^ 2)
>
> i) plsr (octane ~ NIR + NIR_sq, ncomp = 10, data = gasTrain, validation =
> "LOO") I (x ^ 2)
> ii)p lsr (octane ~ NIR + I(NIR^2), ncomp = 10, data = gasTrain, validation
> = "LOO") I (x ^ 2)
> iii)p lsr (octane ~ NIR : I(NIR^2), ncomp = 10, data = gasTrain, validation
> = "LOO") I (x ^ 2)
> iv)p lsr (octane ~ NIR * I(NIR^2), ncomp = 10, data = gasTrain, validation
> = "LOO") I (x ^ 2)
>
> 2 - For your regression, did you plan to use MARS instead of PLS?
>
>
>
>
> Dear all,
>> I am using the pls package of R to perform partial least square on a set of
>> multivariate data. Instead of fitting a linear model, I want to fit my
>> data with a quadratic function with interaction terms. But I am not sure
>> how. I will use an example to illustrate my problem:
>> Following the example in the PLS manual:
>> ## Read data
>> data(gasoline)
>> gasTrain <- gasoline[1:50,]
>> ## Perform PLS
>> gas1 <- plsr(octane ~ NIR, ncomp = 10, data = gasTrain, validation = "LOO")
>> where octane ~ NIR is the model that this example is fitting with.
>> NIR is a collective of variables, i.e. NIR spectra consists of 401 diffuse
>> reflectance measurements from 900 to 1700 nm.
>> Instead of fitting with octane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ...
>> I want to fit the data with:
>> octane[i] = a[0] * NIR[0,i] + a[1] * NIR[1,i] + ... +
>> b[0]*NIR[0,i]*NIR[0,i] + b[1] * NIR[0,i]*NIR[1,i] + ...
>> i.e. quadratic with interaction terms.
>> But I don't know how to formulate this.
>> May I have some help please?
>> Thanks,
>> Kelvin
>
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>
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