similar to: In svm(), how to connect quantitative prediction result to categorical result?

Hi, My R version is old, and I would like to update it. How can I update R and keep all the libraries? Thanks Best, Yunfei Li -------------------------------------------------------------------------------------- Research Assistant Department of Statistics & School of Molecular Biosciences Biotechnology Life Sciences Building 427 Washington State University Pullman, WA 99164-7520 Phone:

Hi, I tried to add a progress bar to my script, but it seems the "txtProgressBar" function will wipe out what I want to print before it(for example - the "Hello" in following script), and the parameters "title" and "label" do not work either. How can I put a title for the progress bar? or is there any other better function I can use? testit <-

All, I am working with NIR spectral data and it was great to find that the example in ?plsr also used spectral data. Unfortunately, I am having difficulty figuring out how the "yarn" dataset is structured to allow for the plsr model to read: library(pls) data(yard) yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV", method = "oscorespls")

I am attempting to create a formula using as.formula for a PLS analysis. I have used the code below successfully, but in a previous R version and with many fewer predictors. Any help getting all of these predictors into one formula would be greatly appreciated. TC.fmla <- as.formula(paste("TC ~ ", paste(vars, collapse= "+"))) As I mentioned, this code worked fine in a

I am attempting to run principal components analysis on a dataset of spectral reflectance (6 decimal places). I imported the data using read.table and there are both column and row headers. When I run princomp I receive the following error: Error in cov.wt(z) : 'x' must contain finite values only Where am I going wrong? Ross

All, I have been using a data set to build pls models for three different soil properties. Two of the three models run fine; however I receive the following error for the final model. > libs.IC.cal <- mvr(libs.IC.fmla, data = libsdata.cond.cal, ncomp=20,validation = "LOO", method = "oscorespls") Error in colMeans(x, n, prod(dn), na.rm) : 'x' must

I am attempting to write a routine where I can run PAM (partition around mediods) on a dataset containing multiple soil cores and PCA spectral data from several depths per core. I want to run PAM on each individual core, so I need to group the data by core to run the analysis. Below is an example of my data structure: Lab.id PC1 PC2 PC3 MAT057.2.5 2.438454966 -1.011182986 -3.040881377

I am new to Linux (not to R) and recently installed Mandriva Spring 2007 on my partitioned hard drive. My next objective is to install R in the Linux environment, unfortunately Mandriva is not one of the Linux distributions available for download... Could someone please let me know which distribution I should use? Thanks. Jonathan --------------------------------- Boardwalk for $500?

Hi, I am trying to read in my Affymetrix CEL files (48 files, total ~600 MB) but I keep getting memory errors. Can somebody please help me with this. Or is therea remote server I can send my data to for computation? Any help is much appreciated. Thanks Dr. Tristan Coram Postdoctoral Research Associate Research Plant Pathologist/Geneticist United States Department of

Hello, I have a small statistics question, and as I'm quite new to statistics and R, I'm not sure if I'm doing things correctly. I am looking at two quantitative variables (x,y) that are correlated. When I divide the data set according to a categorical variable z, then x and y are more poorly correlated when z = A than when z = B (see attached figure). In fact x and y are two

1. You have levels(A) as "2" and "4", yet you showed equations for A=0 and A=1? 2. y = A + X + A*X means you're allowing the different groups of A to have different slopes. Probably not what you intended. 3. It's probably best to provide a small sample of the data (and R code) so we know how you got what you got. Andy From: Ben Ridenhour > > Hello, >

Dear all, I have a question about using categorical predictors for SVM, using "svm" from library(e1071). If I have multiple categorical predictors, should they just be included as factors? Take a simple artificial data example: x1<-rnorm(500) x2<-rnorm(500) #Categorical Predictor 1, with 5 levels x3<-as.factor(rep(c(1,2,3,4,5),c(50,150,130,70,100))) #Catgegorical Predictor

Hi All, Here is the case. I want to build classification model (SVM). Some of variables for this model are categorical attributes which represent words (usually 3-10 words - query for search in google). For example: search_id | query_words |..| result -----------+----------------------------------+--+-------- 1 | how,to,grow,tree |..| 4 2

Hello, I would like to post the below position on your site. Thanks, Quantitative Analyst/Quantitative Developer MSIM Global Risk & Analysis, Quantitative Research & Model Review group Morgan Stanley Investment Management (MSIM), together with its investment advisory affiliates, has more than 680 investment professionals around the world and approximately $279 billion in assets under

All, I am a new user to Linux but I am familiar with R. I have previously used and installed R on a Windows platform without problems. I recently set up a dual boot system (XP_64, Mandriva) to run R on a Linux platform in order to more efficiently handle large datasets. I have not done compiling before, but read the R instructions and followed to my best ability. I downloaded the most recent

Hi all, I want to conduct a cluster analysis with quantitative variables. More precisely, it concerns binary and non-ordered categorical variables. For such data, various similarity measures have been proposed, such as the Jaccard index or the simple matching index. So, is there a package such as mva or multiv in the case of quantitative variables? Could you indicate me reviews, papers or

Hi, I am having difficulties in finding ways to analyse scatter plots and quantitatively differentiate between them. Since this is best demonstrated by a figure, I have attached one with 4 subfigures (a)-(d). Qualitatively (and I hope you would agree with me), sub-figures (a), (b), and (d) seem to represent uniform 2d scatters. It is hard for me to quantify it, but it seems as if the envelopes

To start ztmonitor in quantitative mode you do the following. Assuming you are running Asterisk V1.0.9 you need to edit one line to ztmonitor.c in /usr/src/zaptel as per patch 2783 http://bugs.digium.com/bug_view_page.php?bug_id=0002783 Change line 261 fprintf(stderr, "Usage: ztmonitor <channel num> [-v] [-f FILE]\n"); to fprintf(stderr, "Usage: ztmonitor <channel

Dear Specialists in R, May be somebody has experiment in using pakage for the book Quantitative Risk Management by McNeil? This package is writen in R. I have run this package for fitting the data to Nornal Inverse Gaussian distribution and fased with following problem. > Return<-read.csv("data.csv") > Transpose<-t(Return) > fit.NH(Transpose, case="NIG",

Hello I'm trying to make boxplots However I'm having issues because my x axis is distance downstream on a river. The boxplots function in r assumes the x axis are categorical variables but I would like my boxplots to show true distance downstream just as it would if I used the plots function. However I want to show the box and whisker diagrams. My distances are not in evenly spaced