similar to: pls.pcr compared to Unscrambler

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 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

Hello, can anyome tell me how to access the full script of the panel.smooth function so that I can change the thickness of the smoothing line or its colour ? All I could access is : > panel.smooth function (x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, col.smooth = "red", span = 2/3, iter = 3, ...) { points(x, y, pch = pch, col = col, bg =

Does anybody know how to make pairs graphics with dots of different colours depending on the value of a categorical variable ? Thanks, Arnaud ************************* Arnaud DOWKIW Department of Primary Industries J. Bjelke-Petersen Research Station KINGAROY, QLD 4610 Australia T : + 61 7 41 600 700 T : + 61 7 41 600 728 (direct) F : + 61 7 41 600 760 **************************

Hello everyone, Does anyone know how to implement the argument "unit" in R's plclust function ? I used to use Splus where this argument exists but it has not been implemented in R's plclust. The reason why I switched from Splus to R is that Ward's method is not implemented for S's hclust whereas it is implemented for R's hclust. What I would need is S's plclust

Dear all, Does anyone know how to run Principal Coordinates Analysis (PCoA) from a squared euclidean dissimilarity matrix with R ? Thanks, ************************* Arnaud DOWKIW Department of Primary Industries J. Bjelke-Petersen Research Station KINGAROY, QLD 4610 Australia T : + 61 7 41 600 700 T : + 61 7 41 600 728 (direct) F : + 61 7 41 600 760 **************************

I have switched from The Unscrambler to R for pls regression analysis and have been able to calculate scores, coefficients, RMSEP from a large number of PLS1 and PLS2 models. The ultimate goal is to use these models for predicting unknown samples, which again is straight-forward with the built-in predict() function. However, I?m struggling with prediction uncertainty (i.e. confidence intervals) on

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

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 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)

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, I want to calculate PLS package in R. Now I want to calculate R, MSEP, RMSEP and R2 of PLSR and PCR using this. I also add this in library of R. How I can calculate R, MSEP, RMSEP and R2 of PLSR and PCR in R. I s any other method then please also suggest me. Simply I want to calculate these value. Thanking you. -- Nitish Kumar Mishra Junior Research Fellow BIC, IMTECH, Chandigarh, India

Hi, Currently I have a dataset of 2400*408. And I would like to apply PCR method to study the any correlation between the tests. My current data is in data.frame and I have formed horizontal(1-407) to be the exact data, and (408) to be my results data(Yes and No) I have also binarized these Yes and No to 1 and -1s. However, when I refer to PCR manual on R, the example of yarn.pcr <-

Dera R helpers, I have a (x,y,z) data file where x and y are spatial coordinates and z a variable. I have some missing values in the z column and I would like to replace them with an optimized estimation from the neighbour cells. I could not find any function in R to do that. Is anybody aware of such function ? Maybe someone knows how to implement the Papadakis method in R... Thanks for your

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.

Dear collegues, I´ ve worked with near infrared (NIR) spectroscopy to assess chemical, physical, mechanical and anatomical properties of wood. I use "The Unscrambler" software to correlate the matrix of dependent variables (Y) with the matrix of spectral data (X) and I would like to migrate to R. The matrix of spectral variables is very large (2345 columns and n lines, where n =

Dear list members, I would like to ask two questions, although the second one must have been answered a thousand times. 1) is there a simple way to estimate the influence of individual variables in PCR and PLS models? What I have in mind is a way to rank the variables according to their influence, using some numerical value of a statistic. 2) what is the proper way to cite a CRAN package? How

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 1.1-0 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