similar to: how to group the output of aov by a variable?

Dear Experts, I need to include the repeated structure in our data set object, recall.sums.df, before using gls function. Thus I used groupedData. But I encountered error messages which may mean '*' is not not meaningful factor. Please let me know what I have to do. Thanks, Jeong > recall.sums.df[0:10, ] recall.values recall.ind subj replication hemi region group 1 17.515

Dear R-help, I'm facing a problem with defining a repeated measures anova with weighted data. Here's the code to reproduce the problem: # generate some data seed=11 rtrep <- data.frame(rt=rnorm(100),ti=rep(1:5,20),subj=gl (20,5,100),we=runif(100)) # model with within factor for subjects/repeated measurements, no problem aov(rt~ti + Error(subj/ti),data=rtrep) #model with weights

Hi all, I have a strange problem and rigth now I can't figure out a solution. Trying to calculate an ANOVA with one between subject factor (group) and one within (hemisphere). My dependent variable is source localization (data). My N = 25. My data.frame looks like this: > ML.dist.stack subj group hemisphere data 1 1 tin left 0.7460840 2 2 tin left

Sorry for reposting, but even after extensive search I still did not find any answers. using: summary(aov(pointErrorAbs~noOfSegments*turnAngle+Error(subj/(noOfSegments+turnAngle)), data=anovaAllData )) with subj being a random factor and noOfSegments and turnAngle being fixed factors, I get the following results: ---------------------------------------------- Error: subj Df Sum

I'm starting to understand fixed and random effects, but I'm puzzled a bit. Here is an example from Hays's textbook (which is great at explaining fixed vs. random effects, at least to dummies like me), from the section on mixed models. You need library(nlme) in order to run it. ------ task <- gl(3,2,36) # Three tasks, a fixed effect. subj <- gl(6,6,36) # Six subjects, a random

Your response nicely clarifies a question that I've had for a long time, but which I've dealt with by giving each subject a unique label. Unless I'm missing something, both techniques should work as the toy example below gives exactly the same output in all 3 cases below (forgetting about the convergence problem). Would there be a reason to prefer labeling the levels one way or

I am using R 1.1 with Redhat 6.2 and RW 1.001 with Win98 (the upkey doesn't work on my IBM either as has been previously reported by others). The function aov doesn't return either the residuals or the residual degrees of freedom for split-plot designs. If you use the following code from Baron and Li's "Notes on the use of R for psycology experiments and questionnaires"

Dear R people, I am having a problem with modeling the following SAS code in R: Class ID Gr Hemi Region Gender Model Y = Gr Region Hemi Gender Gr*Hemi Gr*Region Hemi*Region Gender*Region Gender*Hemi Gr*Hemi*Region Gender*Hemi*Region Gr*Gender*Hemi*Region Random Intercept Region Hemi /Subject = ID (Gr Gender) I.e., ID is a random effect nested in Gr and Gender, leading to ID-specific

I am really new to R and would appreciate some help to sort out a problem with data extraction from a large file. I have entered the following command to filter data from a large data set called "upanddown" such that those records in upanddown corresponding to blockType=3'UTR are transferred to a new file called "new": new<-sqldf("select * from upanddown where

Dear *, I have to perform an ANOVA analysis to study the effects on the performance of some algorithmic components in the design of my algorithm. I have done a full factorial design for the following 5 factors (my response variable is "best"): $ model :'data.frame': 3360 obs. of 6 variables: ..$ best : num [1:3360] 0.108 0.573 0.625 1.057 0.451 ...

Hello, I am writing down a general function to implement the bootstrapF method for repeated measures anova. I am passing the function several data frames: y = dependent subj = subject identifiers b = between-subjects factors (number = NB) w = within-subjects factors (number = NW) after grouping of all these variables in a single data frame the aov() call looks like this:

Hi all, I've been reading about aov() at http://www.psych.upenn.edu/~baron/rpsych/rpsych.html and http://davidmlane.com/hyperstat/intro_ANOVA.html and I try to use this test in experiments with my simulator. What I would like Anova to tell me is whether the differences I see when plotting the means of performance per method are significant. And also, whether this is dependent on the problem

Dear Ladies and Gentlemen! On a CD-ROM-server at the University Koblenz-Landau we are using Samba (2.0.5a) with Red Hat Linux (6.0, Kernel 2.2.5). The server is located here in Koblenz, but also used from another department of the university at Landau (about 80 miles from Koblenz; they have their own class B network). A traceroute from both locations gave a response time of about 10-50 msec. On

Hi, I've recently install glusterfs on a cluster of several machines, and although it works very nicely when I use TCP connections I completely fail to use RDMA connections. I have 10 machines with IB NICs, and RDMA does work between them (verified using ib_write_bw -r and several other apps). When I configure a volume with RDMA I have a problem with the port at the client side. It keeps

hi, another anova question !! how does aov() treat missing values ? since it might depend on my experimental design: i have a within subject design with two factors ... i use aov() like this: aov(performance~type*block+Error(subj/(type+block)),data=anovaData) now it might happen that i have a couple of NaN data points in my result dataFrame. how can i know/control how aov() is treating those

Hi All, I am using samba version 2.2.6 on an Irix OS. My problem is that I have no permission to read/write into dot files. when I access the data via Dos and edit the files I receive "read only file" The files look great from the Unix side (777 permissions) This is my smb.conf file: # Samba config file created using SWAT # from 172.17.5.221 (172.17.5.221) # Date: 2003/04/01 19:09:50

Hi, I'm interested in doing a repeated measures anova using aov. The procedure is nicely described in section 6.7.1, pp. 24-27 of Baron and Li's "Notes on the use of R for psychology experiments and questionnaires," and I've reproduced their example exactly. My own problem is almost identical to theirs: rawdat<-c(1.6530074e+001, 1.2124254e+001, 1.0040371e+001,

Hi, yet another anova.mlm problem - it doesn't seem to end. This time, I have a setup with a few within-subject factors and a between-subject factor (SGROUP). Consider the most simple case with only one within-factor (apo): > mlmfit0 <- lm(data.n ~ 0 + SGROUP) > mlmfit1 <- lm(data.n ~ 1 + SGROUP) > anova(mlmfit1,mlmfit0,test="Spherical",M=~hemi,X=~1) Analysis of

On 4/26/19 3:50 AM, Gary Stainburn wrote: > I can't remember the other one. I have removed all of the manual amendments so am now basically set up as initially installed. This is my process for fail2ban: 1: "yum install fail2ban"? This installs fail2ban and fail2ban-firewalld. 2: install /etc/fail2ban/jail.local.? This file enables the matching rules in

On Mon, March 9, 2015 13:11, John Plemons wrote: > Been working on fail2ban, and trying to make it work with plain Jane > install of Centos 7 > > Machine is a HP running 2 Quad core Xeons, 16 gig or ram and 1 plus TB > of disk space. Very generic and vanilla. > > Current available epel repo version is fail2ban-0.9.1 > > Looking at the log file, fail2ban starts and stops