Displaying 20 results from an estimated 2000 matches similar to: "How to formulate quadratic function with interaction terms for the PLS fitting model?"
2017 Jul 13
0
How to formulate quadratic function with interaction terms for the PLS fitting model?
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
2017 Jul 16
2
How to formulate quadratic function with interaction terms for the PLS fitting model?
> On Jul 13, 2017, at 7:43 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
>
> 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
>>
2017 Jul 16
0
How to formulate quadratic function with interaction terms for the PLS fitting model?
??
If I haven't misunderstood, they are completely different!
1) NIR must be a matrix, or poly(NIR,...) will fail.
2) Due to the previously identified bug in poly, degree must be
explicitly given as poly(NIR, degree =2,raw = TRUE).
Now consider the following example:
> df <-matrix(runif(60),ncol=3)
> y <- runif(20)
> mdl1 <-lm(y~df*I(df^2))
> mdl2
2017 Jul 13
0
Quadratic function with interaction terms for the PLS fitting model?
> On Jul 13, 2017, at 10:43 AM, Bert Gunter <bgunter.4567 at gmail.com> 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 <-
2017 Jul 13
4
Quadratic function with interaction terms for the PLS fitting model?
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.
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom
2017 Jul 13
2
Quadratic function with interaction terms for the PLS fitting model?
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
2017 Jul 13
0
Quadratic function with interaction terms for the PLS fitting model?
> On Jul 12, 2017, at 6:58 PM, Ng, Kelvin Sai-cheong <kscng at connect.hku.hk> wrote:
>
> 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
2017 Jul 13
0
Quadratic function with interaction terms for the PLS fitting model?
Bert,
The 'degree' argument follows the "..." argument in the function declaration:
poly(x, ..., degree = 1, coefs = NULL, raw = FALSE, simple = FALSE)
Generally, any arguments after the "..." must be explicitly named, but as per the Details section of ?poly:
"Although formally degree should be named (as it follows ...), an unnamed second argument of length 1
2017 Jul 13
2
Quadratic function with interaction terms for the PLS fitting model?
Marc:
1. I am aware of the need to explicitly name arguments after ... --
see the R Language definition where this can be inferred from the
argument matching rules.
2. I am aware of the stated exception for poly(). However:
> x1 <- runif(20)
> x2 <- runif(20)
> mx <- cbind(x1,x2)
> poly(mx,2)
Error in poly(dots[[i]], degree, raw = raw, simple = raw) :
'degree'
2017 Jul 12
0
How to formulate quadratic function with interaction terms for the PLS fitting model?
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
2009 Oct 22
1
data frame is killing me! help
Usage
data(gasoline)
Format
A data frame with 60 observations on the following 2 variables.
octane
a numeric vector. The octane number.
NIR
a matrix with 401 columns. The NIR spectrum
and I see the gasoline data to see below
NIR.1686 nm NIR.1688 nm NIR.1690 nm NIR.1692 nm NIR.1694 nm NIR.1696 nm
NIR.1698 nm NIR.1700 nm
1 1.242645 1.250789 1.246626 1.250985 1.264189 1.244678 1.245913
2008 May 11
1
Fundamental formula and dataframe question.
There is a very useful and apparently fundamental feature of R (or of
the package pls) which I don't understand.
For datasets with many independent (X) variables such as chemometric
datasets there is a convenient formula and dataframe construction that
allows one to access the entire X matrix with a single term.
Consider the gasoline dataset available in the pls package. For the
model
2017 Jul 13
0
Quadratic function with interaction terms for the PLS fitting model?
Hi Bert,
Ok, to your initial point, the key nuance is that if 'x' is a vector, you can leave the 'degree' argument unnamed, however, if 'x' is a matrix, you cannot. That aspect of the behavior does not seem to change if poly() is called stand alone or, as suggested in ?poly, within a formula to be parsed.
Working on tracing through the code using debug(), the error is
2011 Jul 21
1
Error: bad index in plotmo functions for MARS model (package earth)
Hello all useRs,
I am tring make a simple surface plot ( 2 by 2 terms of a MARS model
(with earth package)
but I get the follow error message:
> plotmo( mars )
Error: bad index (missing column in x?)
I don't no how to workround this... :-(
I thanks in advanced by some help!
Thanks.
Cleber
###############
>
> ### example code:
> library( earth )
> data( gasoline,
2011 May 12
1
Fw: Help with PLSR
Hi
I am attempting to use plsr which is part of the pls package in r. I
amconducting analysis on datasets to identify which proteins/peptides are
responsible for the variance between sample groups (Biomarker Spoting) in a
multivariate fashion.
I have a dataset in R called "FullDataListTrans". as you can see below the
structure of the data is 40 different rows representing a
2011 May 18
1
Help with Memory Problems (cannot allocate vector of size)
While doing pls I found the following problem
> BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata, jackknife =
>FALSE, validation = "LOO")
when not enabling jackknife the command works fine, but when trying to enable
jackknife i get the following error.
>BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata, jackknife =
>TRUE,
2011 May 17
1
Help with PLSR with jack knife
Hi
I am analysing a dataset of 40 samples each with 90,000 intensity measures for
various peptides. I am trying to identify the Biomarkers (i.e. most significant
peptides). I beleive that PLS with jack knifing, or alternativeley
CMV(cross-model-validation) are multivariateThe 40 samples belong to four
different groups.
I have managed to conduct the plsr using the commands:
BHPLS1 <-
2009 Aug 28
2
Pls package
Hi,
I have managed to format my data into a single datframe consisting of two AsIs response and predictor dataframes in order to supply the plsr command of the pls package for principal components analysis.
When I execute the command, however, I get this error:
> fiber1 <- plsr(respmat ~ predmat, ncomp=1, data=inputmat,validation="LOO")
Error in model.frame.default(formula =
2017 Dec 05
2
PLS in R
Hello, I need help with a partial least square regression in R. I have read
both the vignette and the post on R bloggers but it is hard to figure out
how to do it. Here is the script I wrote:
library(pls)
plsrcue<- plsr(cue~fb+cn+n+ph+fung+bact+resp, data = cue, ncomp=7,
na.action = NULL, method = "kernelpls", scale=FALSE, validation = "LOO",
model = TRUE, x = FALSE, y =
2010 Jul 08
4
Column header strategy
Hopefully simple question: What is the best way to name, and treat factor
columns for data that has lots of columns?
This is my column list:
id pID50 D.1 D.2 D.3 D.4 D.5 , etc. all the way to D.185
I was under the impression from several R examples in pls that if you name
your columns like above, you should be able to simply call all the D factors
with "D", instead of going in and