similar to: another ANOVA/LM question

Full_Name: Tanya Logvinenko Version: 1.7.0 OS: Windows 2000 Submission from: (NULL) (132.183.156.125) For unbalanced design, I ran into problem with ANOVA (aov function). The sum of squares for only for the second factor and total are computed correctly, but sum of squares for the first factor is computed incorreclty. Changing order of factors in the formula changes the ANOVA table. For the

Dear R users, I posted a couple of questions and got no response, so I am giving it another shot. I ran an experiment with a TWO-WAY within subject design. A sample dataset is in http://www-scf.usc.edu/~hex/data.txt I already ran ANOVA by using the following formula: aov(RT~Factor1*Factor2 + Error(Subject/(Factor1*Factor2)), data=data) and I obtained the following information: -------------

Dear R people, I have a couple of questions about post-doc analyses for 2 by 2 within subjects ANOVA. I conducted a psycholinguistic study that combined a 2 by 2 design and a latin square design. Specifically, I had 32 items each of which generated 4 conditions. Participants saw each of the 32 items only once: 8 in Condition A, 8 in B, 8 in C, and 8 in D. The table below serves as an example.

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,

G'day, Plot doesn't seem to work when I specify block as an error term, but does if I load block into the model as the first factor (see code below). Is there some way to make plot work with the fancier model specification? Also, if anyone knows how to get SEDs for this model I would really like to know. response<-c(rnorm(5,5,2),rnorm(5,12,2),rnorm(5,18,2),rnorm(5,12,2))

Dear list, I have a question regarding the meaning of intercept term in a two-way anova model without interaction term. for example (let's assume there is no interaction between factor1 and factor2) : > df         val        factor1 factor2 1  48.61533       A      t1 2 171.13535       B      t1 3  65.96884       C      t1 4  63.71222       A      t2 5  80.22049       B      t2 6 

Hello, as far as I see, R reports type I sums of squares. I'd like to get R to print out type III sums of squares. e.g. I have the following model: vardep~factor1*factor2 to get the type III sum of squares for factor1 I've tried anova(lm(vardep~factor2+factor1:factor2),lm(vardep~factor1*factor2)) but that didn't yield the desired result. Could anyone give me a hint how to proceed?

Hi, I use anova() to find interaction effect and then I need to find data with main effect from those data with interaction effect. How to do that? I used : anova(lm(data~factor1*factor2)), then select data with interaction effect. Then I need to select those data also with main effect of factor1 or factor2, from previous selected data. How to do that? Many thanks for your time on my

Dear list, im facing an issue of statistical data analysis that I consider myself unable to resolve in R so i hope to get some valuable insights from you. i run an ANOVA with four factors; factor4 is an between factor (two different groups measured), the others are withins (tested across /all/ subjects). accordingly, my model looks as follows: fm1

Is names(model1$coef) what you're looking for? -----Original Message----- From: Kenneth Cabrera [mailto:krcabrer at epm.net.co] Sent: 29 November 2002 10:36 Cc: R-help at stat.math.ethz.ch Subject: [R] Obtaining the variable names of a glm object Hi, R users! Suppose I make a model like this:

Dear all, I use a nested design with lm and glm, with factor2 nested within factor1. In order to test for the significance of both factors, I use anova tables on the obtained models such as follows: /> mod1<-lm(A~factor1/factor2) > amod1<-anova(mod1, test="F") Analysis of Variance Table Response: A Df Sum Sq Mean Sq F value Pr(>F) factor1

Hello there, I have a situation where I would like to select the first row of a particular factor for a data frame (data example below). So that is, I would like to select the first entry when the factor1 =A and then the first row when factor1=B etc. I have thousands of entries so I need some general way of doing this. I have a minimal example that should illustrate what I am trying to do. I am

Dear all, I want to improve my adj - R sq. I 've chequed some established models and they introduce two times the same variable, one transformed, and the other not. It also improves my adj - R sq. But, isn't this bad for the collinearity? Do I interpret coefficients as usual? Estimate Std. Error t value Pr(>|t|) (Intercept) 1.73140 7.22477 0.240

I can prove I've done this before, but I recently installed Rexcel (and it was easiest to reinstall R and some other bits to make it work) and now its no longer working. Before I would do an ANOVA and a tukey post-hoc like this: >data1.aov=aov(result~factor1*factor2, data=data1) then... >TukeyHSD(summary(data1.aov)) and it would give me a nice tukey table of all the pairwise

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

Hi All, I have a 4-dimensional data. I'm using barchart() function from lattice package. The R code and data are below - code includes one for stack=TRUE and other for stack=FALSE. I would like to present the data in another form which would be plotting Factor3 levels (P, Q, R, S) as two stacked bars (side by side). Like, for each level of Factor1 there should be two bars: first bar showing

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

Buenas, En R, como en la mayoría del software estadístico, no se utiliza ningún nivel de confianza sino que lo que se calcula es el p-valor asociado al contraste. De forma que cuanto más cerca de 0 esté el p-valor "menos credibilidad le damos a la hipótesis nula". Dicho mejor, debemos rechazar la hipótesis nula si el p-valor está por debajo de nuestro nivel de confianza. Por ejemplo,

Hi All, I have googled and tried finding if someone has ever tried producing (Grouped + Stacked) Barplot. I couldn't find one. My data needs to be reshaped, but once it is done it would be something like this: Factor1 Factor2 Factor3 Value A X P 10 A X Q 20 A Y P 20 A Y Q 5 A Z P 20 A Z Q 10 B X P 20 B X Q 10 B

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little