similar to: three short questions

I have a factor (with "n" observations and "k" levels), with only "nobs" < n of the observations not missing. I would like to produce a (n x k) model matrix with treatment contrasts for this factor, with rows of NAs placeholding the missing observations. If I use model.matrix() I get back a (nobs x k) matrix. Is there an easy way to get the (n x k) without

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1

Greetings, This seems too rudimentary to ask but for the life of me I cannot locate a readily easy method to compute the univariate mode. I know "mode" is not correct and "table" provides a reasonable count but I figured there would be an easy way to extract the value from the table after I do something like: max(table(mydadat$myvar)) unfortunately it only returns the max

Can a single random number be generated in R? I have an exercise that wants to simulate coin tosses, and I cannot seem to find a good example of the use of random number generation in R. Any help? Joshua Gramlich Chicago, IL

I want to subset the dataframe based on certain values in a row. for each row in my dataframe if ANY one value of a particular set of columns satisfies cond append a logical value true at the end of the row else append a false at the end of the row in the end I want to be able to subset the whole data based on the appended true or false value. I could literally code like this, but I think

If you found my subject heading to be confusing then I'm sure you'll enjoy the example I've included below. I find the apply type functions to be wonderful for avoiding loops but when I use them with existing functions, I end up using loops anyway to extract the vectors I want. I would appreciate it if someone could show me how to avoid these loops. Thanks. EXAMPLE:

without having to check the vector element by element? Thanks a lot! Jason ===== Jason G. Liao, Ph.D. Division of Biometrics University of Medicine and Dentistry of New Jersey 335 George Street, Suite 2200 New Brunswick, NJ 08903-2688 phone (732) 235-8611, fax (732) 235-9777 http://www.geocities.com/jg_liao

In general this is what "apply" does. In your example you could use rowMeans(ar) as well, I think. Reid Huntsinger -----Original Message----- From: Olivier Martin [mailto:olivier.martin at inrialpes.fr] Sent: Tuesday, July 02, 2002 10:55 AM To: r-help Subject: [R] mean and array Hi all, I have an array, one say ar, with 3 dimensions. dim(ar) >200 3 4 I would like to

Hi Helpers: I have a factor object that has 314k entries of 39 land cover types. (This object can be coerced to characters neatly should that be easier to work with.) > length(foo) [1] 314482 > foo[1:10] [1] Montane Chaparral Barren Red Fir Red Fir [5] Red Fir Red Fir Red Fir Red Fir [9] Red Fir Red Fir 39 Levels:

Dear all, Sorry to bother you with one more newbie question. I have a dataobject with several hundreds of columns. I want to remove columns with a mean of the column values below a certain value: > a<- c(1,2,3,4,5,6) > b<-c(2,4,6,8,10,12) > c<- c(3,6,9,12,15,18) > test<- as.matrix(cbind(a, b, c)) > mean(a) [1] 3.5 > mean(b) [1] 7 > mean(c) [1] 10.5 Say the

Dear all, I observed that the R process is growing in time (I was working with the same session for two days, and the size reached 300 Mb). Explicit calls to 'gc()' did show a trigger around 90 Mb but did not change the RSS (while I remembered that my linux was nicely behaving before and the process size was shrinking when R was freeing memory...). I ended up saving the session, ending it

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather

Hello R-users, I need to create a vector inserting an 1 after each value of another vector. For example: vec1<-c(2,3,4) I need to create a vector with the values 2,1,3,1,4 Does anyone know how create this vector without loops (vec1 could have 1000 elements) Thank you, Juan -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi there, I want to create plots with some kind of 'complex' axis labelling of the form $\dot{V}$ and integrate those plots into a LaTeX document. What is the best way to go? I've figured out, that the 'plotmath' commands are somewhat limited (at least I wasn't able to get the $\dot{V}$ generated) and that plots with 'plotmath'-axes wouldn't export

I am trying to construct a graph of 6 box plots of blood pressures. I want them to be on a single set of axis and I want the SBP to be ontop of the DBP. I have an array bp with the data in it and I tried a[1,]<-c(145,60,147,62,140,57) a[2,]<-c(160,75,160,74,160,70) a[3,]<-c(140,55,140,65,142,55) boxplot(data.frame(a), main = "Blood Pressures", at=c(1,1,2,2,3,3),

Hello, I have a dataframe with several hundred variables. I would like to explore updates of some baseline lme fit by including each of some subset of these variables, one at a time. For various reasons it is inconvenient to rely on the positions of the numbered columns in the dataframe. Here is what I want to do: mod.baseline<-lme(fixed=foo,data=dat,random=bar) for(thisvar in vars){

I may be missing something obvious here, but consider the following simple dataset simulating repeated measures on 5 individuals with pretty strong between-individual variance. set.seed(1003) n<-5 v<-rep(1:n,each=2) d<-data.frame(factor(v),v+rnorm(2*n)) names(d)<-c("id","y") Now consider the following two linear models that provide identical fitted values,

Dear R-experts, I hope that question will not be too redundant (sorry if it is) but i don't seem able to find the answer i need in the archives... I try to create a file which would have 1.several pages and 2.several plots by page. I know how to make a pdf file this way, but my problem is that the pdf size gets way too big and cannot be open. So my question is: - is there a way to diminish

I?ve been using this routine for several years. I?m sorry, I don?t remember where I got it. It works as it should, viz. it blanks the R console. But it requires package rcom and now that requires rscproxy. cls <- function () { require(rcom) wsh <- comCreateObject("Wscript.Shell") comInvoke(wsh, "SendKeys", "\f") invisible(wsh) } > cls()