similar to: Variable Importance in pls: R or B? (and in glpls?)

Hi all, I would like to do some QSAR analysis (quantitative structure activity relationship). I need to use some Partial Least Squares (PLS) regression, but I have not seen this option on the R-project. Is it possible to do this kind of regression on R? thank you in advance best regards, olivier [[alternate HTML version deleted]]

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: > > >

Dear R-helpers, I have performed a PLS regression with the mvr function from the pls.pcr package an I have 2 questions : 1- do you know if mvr automatically centers the data ? It seems to me that it does so... 2- why in the situation below does the output say that the optimal number of latent variables is 4 ? In my humble opinion, it is 2 because the RMS increases and the R2 decreases when 3 LVs

Dear list, I have a question concerning the above mentioned methods in the pls package with respect to the loadings matrix produced by the call. In some work I am doing I have found that the values produced are nearly of the same magnitude but of opposite sign. When I use the example data (sensory) I find this result reproduced. I am prepared to work this through but I have a feeling that

Version 1.0-3 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.0-3 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

hi R help group, I have installed PLS package in R and use it for princomp & prcomp commands for calculating PCA using its example file(USArrests example). But How I can use PLS for Partial least square, R square, mvrCv one more think how i can import external file in R. When I use plsr, R2, RMSEP it show error could not find function plsr, RMSEP etc. How I can calculate PLS, R2, RMSEP, PCR,

Version 1.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.2-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.2-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 2.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls, wide kernel pls, and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and

Version 2.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls, wide kernel pls, and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and

Dear R-helpers, Has anybody ever tried to compare pls regression outputs from the pls.pcr R-package developped by Wehrens with outputs from the Unscrambler software developped by CAMO company ? I find very different outputs and wonder if this comes from differences between methods/algorithms SIMPLS (pls.pcr) and PLS1 (Unscrambler). Arnaud ************************* Arnaud DOWKIW Department of

Hello, I am a beginner in R, and I wonder if anyone could help me with a partial least square regression in R. I have looked up the instructions and the manual from Bjorn Mevi and Ron Wehrens. However, I think I managed to write the script correctly, but I dont understand the output on the R environment, and also how to decide on the number of components to use (from the RMSEP), and also how to

Hello! I need help to use the package pls.pcr in R. I installed R in an IRIX 6.5, using the version of R 0.64.1 from sgifreeware(I didn't get to install the newest version using make). I need to use the package pls.pcr and when I give the command: # R R : Copyright 1999, The R Development Core Team Version 0.64.1 (May 8, 1999) R is free software and comes with ABSOLUTELY NO

How do you install a package from CRAN ? I want to install pls.pcr, so I have downloaded pls.pcr_0.1.1.tar.gz but when I try to install it using the install package(s) from local zip file(s) option it says : > install.packages(choose.files('',filters=Filters[c('zip','All'),]), .libPaths()[1], CRAN = NULL) Error in file(file, "r") : unable to open connection

Dear list I am using the mvr function of the package pls.pcr to compute PLS resgression using a X matrix of gene expression variables and a Y matrix of medical varaibles. I would like to obtain the R2 (sum of squares captured by the model) and Q2 (proportion of total sum of squares captured in leave-one-out cross validation) of the model. I am not sure if there are specific slots in the

Hi everybody! Is there any package or functions to make Partial Least Squares analysis with R? Thanks a lot Luis -------------------------------------------- sapo.pt/kitadsl -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Dear all, I found pls.pcr package will give different results if the data are centered and scaled using scale(). I am not sure about when I should scale my data, and whether the dependent variable should be scaled. If the dependent variable is scaled, how I give a prediction to the real data? I appreciate for any suggestions and comments. Best regards, Jinsong ===== (Mr.) Jinsong Zhao Ph.D.