similar to: Correcting for missing data combinations

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 list, I have two lists: one with data and one with TRUE/FALSE values for data I want to further analyze (see example below). I have been able to use a for loop to extract the data that I want to keep, but think that there probably exists a way to do it without a loop. Any ideas? #sample data set.seed(100) example = list(letters[1:10], letters[1:10], letters[1:10]) ind =

I have very large data sets given in a format similar to d below. Converting these to a data frame is a bottleneck in my application. My fastest version is given below, but it look clumsy to me. Any ideas? Dieter # ----------------------- len = 100000 d = replicate(len, list(pH = 3,marker = TRUE,position = "A"),FALSE) # Data are given as d # preallocate vectors pH =rep(0,len) marker

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.

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

Hi there, I am not getting anywhere near a solution, so here is my question. I have searched the archives and didn't find a solution, but maybe my search didn't use the right words. So here it is: I have a dataframe with the following structure factor1 factor2 value In the (rather large) dataset I have to work on, there are some factor combinations missing, e.g. factor1 <-

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

I'm looking for an R function that simply recodes a quantitative variable into a number of classes according to specified break-points. Obviously I can do this using nested ifelse() commands, but I want to write it into a function where I can't pre-specify the number of classes. Is there an obvious way to do this? An example to clarify: how to convert c(0,10,5,1,9,6) to

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,

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))

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