search for: rjeffri

...ling the rows where a$y==0, i'm getting where they're 0, OR NA. ? Again I feel like either something was changed when I wasn't looking.. or I'm reaaaaaaly forgetting something important. Thanks, Robin Jeffries MS, DrPH Candidate Department of Biostatistics, UCLA 530-633-STAT(7828) rjeffries@ucla.edu [[alternative HTML version deleted]]

...ast observation per ID. For the ID list above, it would look like 1 1 2 3 4 6 7 7 8 9 I haven't worked with indexes/data manipulation much in R, so any pointers would be helpful. Many thanks! ~~~~~~~~~~~~~~~~~~~ -Robin Jeffries Dr.P.H. Candidate in Biostatistics UCLA School of Public Health rjeffries@ucla.edu 530-624-0428 [[alternative HTML version deleted]]

...st), unique(svc$vc))) for (i in 1:length(unique(svc$st))) { for (j in 1:length(unique(svc$vc))){ lookup.svc[i,j] <- svc[svc$st == unique(svc$st)[i] & svc$vc == unique(svc$vc)[j], 4] }} Thanks, Robin ~~~~~~~~~~~~~~~~~~~ -Robin Jeffries Dr.P.H. Candidate UCLA School of Public Health rjeffries@ucla.edu 530-624-0428 [[alternative HTML version deleted]]

...ssign to the task? I know that i can open a command prompt, type R, and then source the file, but I don't know how to pass multiple line arguments to the command line in a system task. Thanks, ~~~~~~~~~~~~~~~~~~~ -Robin Jeffries Dr.P.H. Candidate in Biostatistics UCLA School of Public Health rjeffries@ucla.edu 530-624-0428 [[alternative HTML version deleted]]

...s sparse matrix "technology". I'd like to confirm with others that I can't use a sparse matrix as a fixed effect? I'm getting an "Invalid type (S4) " error. Thanks. ~~~~~~~~~~~~~~~~~~~ -Robin Jeffries Dr.P.H. Candidate in Biostatistics UCLA School of Public Health rjeffries@ucla.edu 530-624-0428 [[alternative HTML version deleted]]

I must be reading the help file for gc() wrong. I thought it said that gc(verbose=FALSE) will run the garbage collection without printing the Ncells/Vcells summary. However, this is what I get: gc(verbose = FALSE) used (Mb) gc trigger (Mb) max used (Mb) Ncells 267097 14.3 531268 28.4 531268 28.4 Vcells 429302 3.3 20829406 159.0 55923977 426.7 I'm embedding this in an

Hello, I need to plot the means of some outcome for two groups (control vs intervention) over time (discrete) on the same plot, for various subsets such as gender and grade level. What I have been doing is creating all possible subsets first, using the aggregate function to create the means over time, then plotting the means over time (as a simple line plot with both control & intervention

I am trying to print a nice looking vector in Sweave. c <- 1:4 I want to see (1, 2, 3, 4) in TeX. . If I use paste(c, ",", sep="") I get "1," "2," "3," "4," If use cat(c, sep=",") I can't seem to assign it to an object, 1,2,3,4> myvec <- cat(c, sep=",") 1,2,3,4> myvec NULL and if I bypass the

I would like to run a logistic regression on some factor variables (main effects and eventually an interaction) that are very sparse. I have a moderately large dataset, ~100k observations with 1500 factor levels for one variable (x1) and 600 for another (X2), creating ~19000 levels for the interaction (X1:X2). I would like to take advantage of the sparseness in these factors to avoid using GLM.

Hello there, I'm still struggling with the *apply commands. I have 5 people with id's from 10 to 14. I have varying amounts (nrep) of repeated outcome (value) measured on them. nrep <- 1:5 id <- rep(c("p1", "p2", "p3", "p4", "p5"), nrep) value <- rnorm(length(id)) I want to create a new vector that contains the sum of the

I know this is a simple question, but I have yet to master the apply statements. Any help would be appreciated. I have a column of probabilities and sample sizes, I would like to create a column of binomial random variables using those corresponding probabilities. Eg. mat = as.matrix(cbind(p=runif(10,0,1), n=rep(1:5))) p n [1,] 0.5093493 1 [2,] 0.4947375 2 [3,]

Hello, I know of some various methods out there to utilize multiple processors but am not sure what the best solution would be. First some things to note: I'm running dependent simulations, so direct parallel coding is out (multicore, doSnow, etc). I'm on Windows, and don't know C. I don't plan on learning C or any of the *nix languages. My main concern deals with Multiple

I have been through the help file archives a number of times, and still cannot figure out what is wrong. I have a tab-delimited text file. 76Mb, so while it's large.. it's not -that- large. I'm running Win7 x64 w/4G RAM and R 2.10.1 When I open this data in Excel, i have 27 rows and 450932 rows, excluding the first row containing variable names. I am trying to get this into R as a

Hello, I'm working with a very large, very sparse X matrix. Let csr.X <- * as.matrix.csr*(X) as described by the SparseM package. The documentation says that "Indexing .... work just like they do on dense matrices". To me this says that I should be able to perform operations on the rows of csr.X in the same way I would on X itself. E.g. f <- function(x){ for (i in 1:n){

What does missInfo compute and how is it computed? There is only 1 observation missing the ethnic3 variable. There is no other missing data. N=1409 > summary(MIcombine(mod1)) Multiple imputation results: with(rt.imp, glm(G1 ~ stdage + female + as.factor(ethnic3) + u, family = binomial())) MIcombine.default(mod1) results se (lower upper)