similar to: calculating effect size for aov or lme

(A repost of this request with a bit more detail) Hi, All. I'd like to calculate effect sizes for aov or lme and seem to have a bit of a problem. partial-eta squared would be my first choice, but I'm open to suggestions. I have a completely within design with 2 conditions (condition and palette). Here is the aov version: > fit.aov <- (aov(correct ~ cond * palette +

Hi, all. I'm sure this is a simple question, but I'm having problems figuring it out myself... I have a table: > currenttable <- table(junk[-1],junk[-n]) > currenttable bar foo junk bar 2 2 0 foo 1 0 0 junk 0 0 1 and I'd like to know the result of the cell currenttable(bar,foo). what is the best way to get that result? thanks!

Dear All, I am in a trouble with reading data. It is in txt file looking like this. 0.00632 18.00 2.310 0 0.5380 6.5750 65.20 4.0900 1 296.0 15.30 396.90 4.98 24.00 0.02731 0.00 7.070 0 0.4690 6.4210 78.90 4.9671 2 242.0 17.80 396.90 9.14 21.60 0.02729 0.00 7.070 0 0.4690 7.1850 61.10 4.9671 2 242.0 17.80 392.83 4.03 34.70 0.03237 0.00

Hi, all. I have a simple design I'm comparing to output from SPSS. the design is 1 repeated measure (session) and 1 between measure (cond). my dependent measure is rl. here is the data I'm using (in a data.frame): mig <- data.frame(subj=factor(rep(subj,3)), cond=factor(rep(cond,3)), session=factor(c(rep(1,nsubj),rep(2,nsubj),rep(3,nsubj))),

Hi, All. I'd have a set of data in an array: process <- c( 5 , 7 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 4 , 1 , 5 , 4 , ...) and I'd like to know the number of transitions in this data. I calculate transitions as the number of times a number follows another number. thus, something like this would be a 1 deep transition: 1 --> 1 : 10% (and actual number of 1 --> 1 occurrences) 1

The message for aov1 was "Estimated effects <may> be unbalanced". The effects are not unbalanced. The design is 'orthogonal'. The problem is that there are not enough degrees of freedom to estimate all those error terms. If you change the model to: aov1 <- aov(RT~fact1*fact2*fact3+Error(sub/(fact1+fact2+fact3)),data=myData) or to aov2 <-

Hi all, To follow up on an older thread, it was suggested that the following would produce confidence intervals for the estimated BLUPs from a linear mixed effect model: OrthoFem<-Orthodont[Orthodont$Sex=="Female",] fm1OrthF. <- lmer(distance~age+(age|Subject), data=OrthoFem) fm1.s <- coef(fm1OrthF.)$Subject fm1.s.var <- fm1OrthF. at bVar$Subject fm1.s0.s <-

hi, all. I use R on a unix (linux) box and am quite happy with it. However, sometimes I need to create a graph that needs to be used with Microsoft Word or Powerpoint (ug). I can create a png or jpeg format picture, but the text look pretty crummy because jpeg is bitmapped. I can also create a PS/EPS version (my preference), but then it doesn't display properly in word or powerpoint (but it

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and

On 31 Mar 01,, R-help Digest wrote (re: R-help Digest V2 #380): > Date: Fri, 30 Mar 2001 09:49:21 -0500 (EST) > From: Greg Trafton <trafton at itd.nrl.navy.mil> > Subject: [R] PICT output? > > hi, all. I use R on a unix (linux) box and am quite happy with it. > However, sometimes I need to create a graph that needs to be used with > Microsoft Word or Powerpoint

On 3 Apr 01,, R-help Digest wrote (re: R-help Digest V2 #382): > - ----- Original Message ----- > From: "Mark Myatt" <mark at myatt.demon.co.uk> > To: "Greg Trafton" <trafton at itd.nrl.navy.mil> > Cc: <r-help at stat.math.ethz.ch> > Sent: Monday, April 02, 2001 9:31 PM > Subject: Re: [R] PICT output? > > > > 2) Is there a way

Hi, all. I'm looking for a way to generate temperature profiles and display them in different colors on different maps. I'm basically looking for a way to display simple meteorological graphs using different color sets within R. Is there a way to do that kind of thing in R? failing that, is there any way to create temperature profiles in R? thanks! greg

hi, All. I'm using R 1.2.2 on a linux box. I have very long labels on my y axis, and when I try to print them out, they always come out clipped so I can't see them. How do I change the margin for the y axis? I've tried mai and mar, but they don't seem to do anything... process.names <- c("Makeprod-data", "Makeprod-QMM", "Search",

Hi, all. I'd like to print out a table to a file (I'm using cat(...,file="foobar")). the problem is, cat doesn't print out the headings for the table (making the table hard to interpret!). For example: > table(showdistribution(nw.109.transitions, interruptions[1])) interruption opened-map 1 2 >

Hi, all. I have a whole group of lists with things like this: nw.1$cond (a string) nw.1$RL (a vector of 10 values) nw.1$IL (a vector of 10 values) and I have lots of these lists: nw.201 nw.202 nw.203 ... what I'd like to do is be able to get specific values from all these lists (like the mean of $RL) for each indivividual list. I can certainly do this manually

<ripley at stats.ox.ac.uk> wrote: > Hint 2: in the absence of balance, ...., and lme can do that Would it be possible to make aov like wrappers to the various special lm variants allowing for a uniform syntax for anova? aov(resp~f1*f2+Error(S/(f1*f2))) ## uses lm aov.lme(resp~f1*f2+Error(S/(f1*f2))) ## uses lme aov.rlm(resp~f1*f2+Error(S/(f1*f2))) ## uses rlm ... I'd do it, but I

I''m a physicist working on fusion energy and dabble in statistics only occasionally, so please excuse gaps in my statistical knowledge. I''d appreciate any help that a real statistics expert could provide. Most people in my field do only very simple statistics, and I am trying to extend some work on multivariate linear regression to account for significant between-group

Dear all, I am running a logistic regression and this is the output: glm(formula = educationUniv ~ brncntr, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max # ???? ????? ?? ???????? -0.8825 -0.7684 -0.7684 1.5044 1.6516 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.06869 0.01155 -92.487 <2e-16 *** brncntrNo

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

Dear all, I'm curious about how to interpret the results of the following code. The first model is directly from the help page of lmer; the second is the same model but using the Gamma family with log link. The fixed effects make sense, because y = 251.40510 + 10.46729 * Days is about the same as log(y) = 5.53613298 + 0.03502057 * Days but the random effects seem quite