search for: ncomps

I don't know the details of pls (in the pls.pcr package, I assume), but if you use validation="CV", that says you want to use CV to select the best number of components. Then why would you specify ncomp as well? Andy > From: ryszard.czerminski at pharma.novartis.com > > When I try to use ncomp parameter in pls procedure I get > following error: > > >

Is it just my installation or bug in 1.9.0 ? The same thing works fine in 1.8.1 Best regards, Ryszard # R-1.9.0 library(pls.pcr) nr <- 8; ndim <- 2 x <- matrix(rnorm(nr*ndim), nrow=nr) y <- as.matrix(x[,1]) for (i in 2:ndim) y <- y + x[,i] y <- y + rnorm(length(y)) m <- pls(x,y,validation='CV') # Error in paste(ncomp, "LV's") : Argument

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

Error in mvr(Kd_nM ~ qsar, ncomp = 6, data = my, validation = "CV", method = "kernelpls") :   Invalid number of components, ncomp How I can fix this? [[alternative HTML version deleted]]

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

Dear list members; The code given below corresponds to the PCA-NIPALS (principal component analysis) algorithm adapted from the nipals function in the package chemometrics. The reason for using NIPALS instead of SVD is the ability of this algorithm to handle missing values, but that's a different story. I've been trying to find a way to improve (if possible) the efficiency of the code,

> 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 >>

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

Dear all,   I want to calculate AIC values of PLSR models. But I find that AIC and extractAIC functions in R could not be used to calculate AIC values of PLSR models. Now I write a section of code(below) to calculate it. But I don't known whether the result is right or not. If I am wrong, please give me some suggestions. Thanks a lot.   Rong Huang   data<-data.frame(

require(ppls) data(BOD) X<-BOD[,1] y<-BOD[,2] Xtest=seq(min(X),max(X),length=200) dummy<-X2s(X,Xtest,deg=3,nknot=20) Z<-dummy$Z Ztest<-dummy$Ztest size<-dummy$sizeZ P<-Penalty.matrix(size,order=2) lambda<-200 number.comp<-3 penalized.pls(Z,y,P=lambda*P,ncomp=number.comp)$coefficients # By default kernel=F

?? 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

I'm new in both R and statistics. I "did my homework", I tried the archives and whatever I managed to get from the sources, but still I need assistance with the plsr package. I have a model with 2 core determinants D1 and D2, made by 3 indicators each (D1a,D1b,D1c and so on). Also I have 2 moderating variables (m1,m2), where m1 moderates D1 and m2 modarates D2. The dependent

> 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 <-

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

Dear helpers please provide me some helpful answer to my problem while I m trying to run a program .I m attaching both the program and the data to which I have to obtain my estimation results. "Motives.dat" is the data file, and "OBTfile4.3" is the complete code of program. by Running this // rawdata<-matrix(scan(inputFile, n = nsubj*ncomp), nsubj, ncomp, byrow = TRUE) \\

hello everybody solved the problem with summary, now I have another one eg I estimate > try.op <- polr( > as.ordered(sod.sit.ec.fam) ~ > log(y) + > log(1 + nfiglimin) + > log(1 + nfiglimagg) + > log(ncomp - nfiglitot) + > eta + > I(eta^2) + >

Hi R-listers, I would like to know how can i extract component no. when the RMSEP is lowest? Currently, I only plot it manually and then only feed the ncomp to the jack knife command. However, I would like to automate this step. Please let me know. Many thanks. Rgrds, [[alternative HTML version deleted]]

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 <-

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,

Hi I'm performing a PLS This is my data present in a file Year Y X2 X3 X4 X5 X6 1960 27.8 397.5 42.2 50.7 78.3 65.8 1960 29.9 413.3 38.1 52 79.2 66.9 1961 29.8 439.2 40.3 54 79.2 67.8 1961 30.8 459.7 39.5 55.3 79.2 69.6 1962 31.2 492.9 37.3 54.7 77.4 68.7 My R-code Data <- read.csv("C:/TestData.csv") variable=names(Data)[4:8] dataset=NULL dataset$X=NULL len=length(variable)