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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. iii) If you want to make the interaction between x and x ^ 2 use the command ":" ->...

...ert 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...

...; wrote: > > poly(NIR, degree = 2) will work if NIR is a matrix, not a data.frame. > The degree argument apparently *must* be explicitly named if NIR is > not a numeric vector. AFAICS, this is unclear or unstated in ?poly. I still get the same error with: library(pld) data(gasoline) gasTrain <- gasoline[1:50,] gas1 <- plsr(octane ~ poly(as.matrix(NIR), 2), ncomp = 10, data = gasTrain, validation = "LOO") Error in rep.int(rep.int(seq_len(nx), rep.int(rep.fac, nx)), orep) : invalid 'times' value > gas1 <- plsr(octane ~ poly(as.matrix(gasTrain$NIR), deg...

...itting 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 c...

...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. &gt...

...uigi 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...

...ial 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...

...variate 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...

...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 var...

...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...

...ial 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...

...nction 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. >>>>>...