similar to: Plot is lost when fitting block as error

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:

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 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.

I can think of many brute-force ways to do this outside of R, but was wondering if there was a simple/elegant solution within R instead. I have a table that looks something like the following: Factor1 Factor2 Value A 11/11/2009 5 A 11/12/2009 4 B 11/11/2009 7 B 11/13/2009 8 >From that I need to generate all permutations of Factor1 and Factor2 and force a 0 for any combination that doesn?t

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

I'd like to get a long data set of minimum values from groups in another data set. The following almost does what I want. (Note, I'm using the word factor differently from it's meaning in R; bad choice of words) myframe = data.frame(factor1 = rep(1:2,each=8), factor2 = rep(c("a","b"),each=4, times=2), factor3 = rep(c("x","y"),each=2, times=4),

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

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?

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

I have a very general question about what the centering option in basehaz does to factors. (basehaz computes the baseline cumulative hazard for a coxph object using the Breslow estimator). Lets say I'm interested in a survival model with two (dichotomous) factors and a continuous covariate. Variable Possible Values Factor1 0 or 1 Factor2 0 or 1

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 

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

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

Hello everyone, I am just a tyro in R and would like your kindly help for some problems which I've been struggling for a while but still in vain. I have a time-series file (with some missing value ) which looks like time[sec] , Factor1 , Factor2 00:00:00 01.01.2007 , 0.0000 , 0.176083 01:00:00 01.01.2007 , 0.0000 , 0.176417 [ ... ] 11:00:00 10.06.2007 , 0.0000 , 0.148250 12:00:00 10.06.2007

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,

I have a data frame with two factors and want to create panel barcharts with one factor defining the panels and the other the vertical categories by which I can count the rows of data in each combination of factors. How do I do this? I have been trying to use barchart(~factor1|factor2) but it does not give the panels as I want them. The data looks like this: Factor1 Factor2 A y B

Hi everybody, I would like to compute the mean for 1 variable between the rows with the same levels. For example, with the dataset below: Factor1 Factor2 Value A X 1 A X 2 A Y 3 A Y 4 B X 5 B X 6 B Y

I am attempting to replicate some of my experience from SAS in R and assume there are best methods for using a combination of summary(), subset, and which() to produce a subset of mean values by categorical or ordinal factors. within sas I would write proc means mean data=dataset; class factor1 factor2 var variable1 variable2; RUN; producing an output with means for each variable by factor

Dear list (probably Hadley), I'm trying to do a plot like the following, composed of bars and error bars: df <- data.frame(factor1=gl(2,5), factor2=gl(5,1), y=rnorm(10), err=0.1) ggplot(df, aes(x=factor1, y=y, fill=factor2)) + geom_bar(position="dodge", stat="identity") + geom_errorbar(aes(min=y-err, max=y+err), position="dodge", width=0.1) The

Hi, I have a data set with two continous variables that I want to plot MEANS (I am not intrerested in median values) on a double-y graph. I also have 2 factors. I want the factor combinations plotted in different panes. Dummy dataset: mydata <- data.frame(factor1 = factor(rep(LETTERS[1:3], each = 40)), factor2 = factor(rep(c(1:4), each = 10)), y1 =