similar to: multistratum glm?

Hello, I am running R under Linux/x11. I would like to call R from a shell script and have it display a series of graphics. The graphics should remain visible until the user clicks or presses a key. I first tried R BATCH, but it does not load the x11 module, making it impossible to open x11 or png devices. Then, I tried to call R with a 'here' script: R --vanilla --quiet --args

Hello, I use the function 'aggregate' a lot. One small annoyance is that it is necessary to name the factors in the 'by' list to get the names in the resulting data.frame (else, they appear as Group.1, Group.2...etc). For example, I am forced to write: aggregate(y,list(f1=f1,f2=f2),mean) instead of aggregate(y,list(f1,f2),mean) (for two factors with short names, it is not such

On Mon, 8 Mar 2004 r-help-request at stat.math.ethz.ch wrote: > Send R-help mailing list submissions to > r-help at stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > or, via email, send a message with subject or body 'help' to > r-help-request at stat.math.ethz.ch > >

Dear All, I am doing some exercise in statistics textbook on comparison of two experimental means. Is it possible to use t.test() do t-test when I have only two means, sample size, two standard deviations ? (no raw data). Thanks. Pramote

Hello Coming from matlab background, I could use some help on this one. I need to generate a data set based on this equation X(t) = 3.8x(t-1) (1-x(t-1)) + e(t), where e(t) is a N(0,0,001) random variable I need say 100 values. How do I do this? Thanks

Hello, I had been thinking for years, without having ever checked (shame on me), that indexing a named vector by a factor 'f' produced the same results as indexing it by 'as.character(f)'. I was wrong, as the following example shows: (m <- c(a=1,b=2)) (f <- factor(c(1,2),labels=c('b','a'))) m[f] m[as.character(f)] m[as.numeric(f)] When the

Hello All, I have a more statistical question, and how this is implemented in R. The problem is the following: We have 2 different solutions (samples), which are filtered and then the concentration of the filtrate is measured. We want to evaluate how the filter proces and the concentration measurement influences the detection of the difference of the two solutions and which step has which

Hi, I'm still trying to figure out that GLM procedure in SAS. Let's start with the simple example: PROC GLM; MODEL col1 col3 col5 col7 col9 col11 col13 col15 col17 col19 col21 col23 =/nouni; repeated roi 6, ord 2/nom mean; TITLE 'ABDERUS lat ACC 300-500'; That's the same setup that I had in my last email. I have three factors: facSubj,facCond and facRoi. I had this pretty

That may sound like a stupid question, but if it confuses me, I'm sure it confuses others as well. I've tried to find that information on the R mail-group info pages, can't seem to find it. Is it something obvious? To begin a brand new discussion, you do your post as an e-mail sent to r-help at stat.math.ethz.ch . As I am doing right now. How do I do an additional post that gets

Hi, I am trying to write a function with the following codes and I would like it to return the values for "alpha beta para parab " seperately. Then I would like to use this funstion for "variable" with factor "a" and "b". But the result turns out to be a matrix with element like "Numeric,2" ... I guess they are just the values for

Is there a conventional way to test for nested factors? I.e., if 'a' and 'b' are lists of same-length factors, does each level specified by 'a' correspond to exactly one level specified by 'b'? The function below seems to suffice, but I'd be happy to know of a more succinct solution, if it already exists. Thanks, Tim. --- "%nested.in%" <-

I'm trying to learn how to do a repeated measures ANOVA in R using lme(). A data set that comes from the book Design and Analysis has the following structure: Measurements (DV) were taken on 8 subjects (SUB) with two experimental levels (GROUP) at four times (TRIAL). In SAS, I use the code: PROC MIXED DATA=[data set below]; CLASS sub group trial; MODEL dv = group trial group*trial;

I have 2 files containing data analysed by 2 different methods. I would like to find out which genes appear in both analyses. Can someone show me how to do this? _________________________________________________________________ [[trailing spam removed]] [[alternative HTML version deleted]]

Dear all R users : I am a IT student with few statistical background and new R user for only have two month exprience. I have a data named medcost, import by read.table() as follow for example (real dataset has 500 cases), the heander id means case id, member means members in a family and cost is the family pay for medical cost every 6 months. id member cost 1 4

This relates to a message from Christophe Pallier to r-help some time ago. Like myself, he finds aggregate very useful, but the interface a little cumbersome. I've implemented a more compact formula interface, found at the bottom of this message: data(ToothGrowth) # I used to aggregate like this: aggregate(list(len=ToothGrowth$len),

Dear all, I would like to take out the values from one vector that are equal to the values in another vector. Example: a <- c(1,2,3,4,5,6,7,8,9) b <- c(3,10,20,5,6) b_noRepeats = c(10,20) So I would like to have the vector b without the same values as vector a. Kind regards, João Fadista [[alternative HTML version deleted]]

I am using R to make two-way ANOVA on a number of variables using g <- aov(var ~ fact1*fact2) where var is a matrix containing the variables. However the outcome seem to be dependent on the order of fact1 and fact2 (i.e. fact2*fact1) gives a slightly (factor of 1.5) different result. Any ideas why this is? Thanks for any help Anders

Hello, I am learning R and I am fluent in Stata and I try to translate part of my Stata code to R to check the reliability of the data under R. I have a proportion variable as a dependent variable pQSfteHT . Independent variables are dummies for two categorical variables called dQSvacrateHTQuali3 and cluster_3. I am fitting a model with the Stata command below: glm pQSfteHT dQSvacrateHTQuali3_2

Hello everyone, I am currently teaching an intermediate stats. course at UCSD Extension using R. We are using Venables and Ripley as the primary text for the course, with Freund & Wilson's Statistical Methods as a secondary reference. I recently gave a homework assignment on logistic regression, and I had a question about glm. Let n be the number of trials, p be the estimated

Dear R-help, I've encounter what seems to me a strange problem with "names<-". Suppose I define the function: fun <- function(x, f) { m <- tapply(x, f, mean) ans <- x - m[match(f, unique(f))] names(ans) <- names(x) ans } which subtract out the means of `x' grouped by `f' (which is the same as, e.g., resid(lm(x~f)) if `f' is a factor).