similar to: One way ANOVA with NO model

Hello all! I have a problem to group my data (years) in 10 years classes. For example for year year decade 1598 1590-1600 1599 1590-1600 1600 1590-1600 1601 1600-1610 --- my is like this> [1] 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 [16] 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 [31] 1628 1629 1630 1631 1632 1633

###Dear R users ###I have been using SensoMineR package from CRAN for most of my work in sensory data analysis and from my usage experience, I encountered some areas for improvement and considered ###modifying the function in SensoMineR package for my personal use. I felt that it could be useful to share this to the community for enabling adoption by other users where they might require a

I am a relatively new user of R, and have a question on ANOVA I have a sumamrised data set of the form (data dummy only) Cell Treatment Total Resp 1 1 25000 682 1 0 35000 600 2 1 5000 40 2 0 4000 30 3 1 36000 2400 3 0 400 250 and wish to perform ANOVA. Because its sumamrised I need to tell R to treat the data as if I had all the individual rows. Otherwise, I only end up with 1 or

In my research area, researchers are very stingy about sharing data. In fact, they are frequently downright secretive about these morsels. Typically, we get fed a diet of summary statistics and the assurance that the data are *normal* without any necessary documentation that they really are. With that as background and assuming that the data really are normal, is there any way in R (or any

Dear all I have found that the two "equivalent" commands do not produce the same results. 1. (I wrote this command by hand, this is what I would do usually) >summary(aov(eduyrs ~ cntry * edf, data=ESS1)) Df Sum Sq Mean Sq F value Pr(>F) cntry 1 257 256.65 21.2251 4.243e-06 *** edf 4 11010 2752.42 227.6296 <

Hello to the R world... I have some problems regarding a GLM - repeated measures analysis. I want to test overall differences between AgeClass and Treatment (between subject) with OpenR1+OpenR2+OpenR3 (repeated measures, within subject). The table looks kind like this: AgeClass Treatment OpenR1 OpenR2 OpenR3 1 1 0 0 12.63 1 1 12.67 3.83 45.67 1 1 38.46 65.38 75.21 1 1 14.46 0 17.96 1 2 27.83

I can't figure out why this function wont work. #Custom Function used fallInBand <- function(x){ #Assume the bands fall go up form 0 to 100 in intervals of 5 then from 100 to 1000 in intervals of 100. #returns the location (band number) if (is.na(x) == FALSE) { if(x < 100) {#bands of 5 if((x %% 5) >=0){#falls within the band Result = as.integer(x/5)+1

Hi.? There appears to be a bug in R function manova.? My friend and I both ran it the same way as shown below (his run) with the shown data set. His results are shown below. we both got the same results.? I was running with R 2.3.1. I'm not sure what version he used. Thanks very much, David Booth Kent State University -----Original Message----- From: dvdbooth at cs.com To: kberk at

I have a data set for different time intervals. The data has three comment lines before data for each time interval. For each time interval there are 500 data points. I want to change the dataset such that I have the following format: t1 t2 t3 ................ 0.00208 0.00417 0.00625 ................. a1 a2 a3 ...................

Hello, This is a standard example in which I read the time series data from a csv file and create a zoo object: x0 <- read.csv(file="CPI.csv", header=TRUE) time_0<-as.yearmon("1981-01")+(0:371)/12 x0zoo<-zoo(x0, time_0) The data look like this: TIME CPI CPI_food CPI_Clothes CPI_House CPI_Rent 198101 62.1 55.34 103.45 65.24 61.43 198102 63.16 56.95

Hi, I am running Fedora 7 VM. On an earlier configuration with zaptel and Asterisk 1.4.21 , meetme worked alright. I upgraded to Dahdi and Asterisk 1.4.26, and the result is choppy sound via Meeme, while a simple Musiconhold works OK with descent audio quality. So I am sure its a Dahdi_dummy problem. Running dahdi_test gave me very poor results. Opened pseudo dahdi interface, measuring

Answering on a mail from >From Keith Wong <keithw_at_med.usyd.edu.au> Date Sun 04 Jul 2004 - 17:21:36 EST Subject [R] Random intercept model with time-dependent covariates, results different from SAS Hi all I've got a question about the degrees of freedom in a mixed model, calculated with lme from the lme4 package. Since I've no access to the original data

Hi Ya'll I'm trying to install the zoo library... I've downloaded about 5 or 6 of their releases (inc. the latest one), all of them giving me the same syntax error when trying to install... Anyone seen this problem before? its not the "lib" argument that is the problem (ive been installing other packages using this exact format with no problem... just zoo is the problem.

Hi everyone - I'm seeing a 32-bit build perform significantly faster (up to 3x) than a 64 bit build on Solaris 8. I'm running R version 2.1.0. Here are some of my system details, and some resulting timings: >uname -a SunOS lonetree 5.8 Generic_117350-16 sun4u sparc SUNW,Sun-Fire-V440 lonetree /home/sgilpin >gcc -v Reading specs from

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

All, I would like to apply a set of rules to each row of the sample data set below. The rule sets are the guidelines for determining an individual's date for retirement eligibility. The rules are found in this document, http://www.opm.gov/feddata/RetirementPaperFinal_v4.pdf. I am only interested in the top two categories for retirement eligibility, the CSRS and FERS plans. The data set has

Hi Tobias and all Polly developers, I have re-evaluated the Polly compile-time performance using newest LLVM/Polly source code. You can view the results on http://188.40.87.11:8000. Especially, I also evaluated our r187102 patch file that avoids expensive failure string operations in normal execution. Specifically, I evaluated two cases for it: Polly-NoCodeGen: clang -O3 -load

Hi, This is the first time I've posted, and I apologize if I formulate this incorrectly. I am analyzing data from a multi-region carrot variety trial. 35 varieties of carrots were grown in 3 randomized complete blocks in organic and conventional fields in Wisconsin, Indiana, Washington, and California. In this example I am comparing the heights of the carrot tops at harvest. In other

Hello folks, TL;DR: This is my proposed hashing interface based on a proposed standard hashing interface. It also is implemented with a much faster and higher quality algorithm than the current one. This is an *early draft* of the code, looking for initial feedback. There has been recent interest in improving the quality and consistency of LLVM's approach to hashing. In particular, getting