similar to: Help - lm, glm, aov results inconsistent with other statistical package

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, >

Hello, I am new to using R and I am having problems get boot() to work properly. Here is what I am trying to do: I have statistic called "cs". cs takes a data matrix (154 x 5) and calculates 12 different scores for me. cs outputs the data as a vector (12 x 1). cs doesn't really use weights, per se, however I have included this as one of the 2 arguments cs can take. I try

Hello all, I'm trying to establish some confidence intervals on predictions I am making using >predict(nls(...)) and predict.nls (unfortunately) does not utilize the se.fit option. A little more background is that I am trying to match the output with older SAS routines to maintain consistency. Because predict.nls does not provide se's for individual predictions, I have been using a

Hello all, I'm sure I'm missing something simple here, but I can't figure out how to modify the glm.fit() function and then get R to use it (sort of). I'm doing something along the lines of: glm.fit<-edit(glm.fit) # add something trivial to the top of the glm.fit function like: print("Hello world!") #now have a modified glm.fit in position 1/.GlobalEnv

Hi all, I have a question regarding how to properly analyze a data set and then how to perform the analysis in R. First, I have data that I would like to analyze using a mixed GLM (I think this is the most appropriate method, but I am unsure). In a mixed model (y = X*beta+Z*gamma+epsilon), I would like to structure the variance matrices of gamma, G, and the error, R, to take advantage of all my

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 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

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

Hi All, this is the fairlock patchset. You can apply them and build successfully. patches are based on linux-next qspinlock can avoid waiter starved issue. It has about the same speed in single-thread and it can be much faster in high contention situations especially when the spinlock is embedded within the data structure to be protected. v8 -> v9: mv qspinlocm config entry to

Hi All, this is the fairlock patchset. You can apply them and build successfully. patches are based on linux-next qspinlock can avoid waiter starved issue. It has about the same speed in single-thread and it can be much faster in high contention situations especially when the spinlock is embedded within the data structure to be protected. v8 -> v9: mv qspinlocm config entry to

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:

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

All, Apparently the attachment did not make it through the list serve. I am having difficulty installing R-2.4.1 on Mandriva Linux 2007 Discovery. Thanks to the help of Doug Bates I got further along the path (Thanks Doug!!). I now cannot figure out which line of code needs modified in order for configure to complete. Please see below for the last several lines of output. checking whether

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 <-

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

Hi, I am studying using SVM functions of e1071 package to do prediction, and I found during the training data are "factor" type, then svm.predict() can predict data directly by categories; but if response variables are "numerical", the predicted value from svm will be continuous quantitative numbers, then how can I connect these quantitative numbers to categories? (for

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")

Hi All, this is the fairlock patchset. You can apply them and build successfully. patches are based on linux-next qspinlock can avoid waiter starved issue. It has about the same speed in single-thread and it can be much faster in high contention situations especially when the spinlock is embedded within the data structure to be protected. v7 -> v8: add one patch to drop a function call

Hi All, this is the fairlock patchset. You can apply them and build successfully. patches are based on linux-next qspinlock can avoid waiter starved issue. It has about the same speed in single-thread and it can be much faster in high contention situations especially when the spinlock is embedded within the data structure to be protected. v7 -> v8: add one patch to drop a function call