similar to: Subsetting with missing data

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 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

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

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

Hallo! I have a vector of ID's like so, id <- c(1,2,2,3,3,3,4,5,5) I would like to create a [start,stop] pair of vectors that index the first and last 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.

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)

Is there an obvious reason why this won't loop to i=2 and beyond? There are many combinations of *st* & *vc* that don't exist in svc. For example, when s=1 there's only an entry at v=1. That's fine, the entry can stay 0. lookup.svc <- array(0,dim=c(length(unique(svc$st)),length(unique(svc$vc))), dimnames=list(unique(svc$st), unique(svc$vc))) for (i in

I want to set up a windows system task that will run a .R script at pre-specified times. Can someone please help with the command line syntax that I would assign 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.

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 read somewhere (help list, documentation) that the random effects in lme4 uses 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

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

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.

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,]

Hi All, my script tries to do the following on factors: > ## Check for case 3: Umsatz = 0 & Kunde = 1 > for (year in 2011:2015) { + Umsatz <- paste0("Umsatz_", year) + Kunde <- paste0("Kunde01_", year) + Check <- paste0("Check_U_0__Kd_1_", year) + + cat('Creating', Check, 'from', Umsatz, "and", Kunde,

Hi Your script is not reproducible. Creating Check_U_0__Kd_1_2011 from Umsatz_2011 and Kunde01_2011 Error in ifelse(Kunden01[[Umsatz]] == 0 & Kunden01[[Kunde]] == 1, 1, 0) : object 'Kunden01' not found > This is interesting x <- c(NA, FALSE, TRUE) names(x) <- as.character(x) outer(x, x, "&") ## AND table <NA> FALSE TRUE <NA> NA FALSE

I stand corrected. The part "If set to 'NULL', it implies 'useNA="always"'." is even in the documentation in R 2.8.0. It was my fault not to check carefully. I wonder, why "always" was chosen for 'useNA' for exclude = NULL. Why exclude = NULL is so special? What about another 'exclude' of length zero, like character(0) (not c(),

Good morning, Is there a way to get table() to default to including NAs - as in... table(..., useNA='ifany') or table(..., useNA='always') or table(..., exclude=NULL) ? I can't see a way under table() or options() or searching the archives (probably using the wrong keyword?). > t1 <- c(1,2,3,3,3,2,NA,NA,NA,NA) > table(t1) t1 1 2 3 1 2 3 I keep forgetting to

This is an example from https://stat.ethz.ch/pipermail/r-help/2007-May/132573.html . With R 2.7.2: > a <- c(1, 1, 2, 2, NA, 3); b <- c(2, 1, 1, 1, 1, 1) > table(a, b, exclude = NULL) b a 1 2 1 1 1 2 2 0 3 1 0 <NA> 1 0 With R 3.3.1: > a <- c(1, 1, 2, 2, NA, 3); b <- c(2, 1, 1, 1, 1, 1) > table(a, b, exclude = NULL) b a 1 2

Hi your initial ds > str(ds) 'data.frame': 2 obs. of 3 variables: $ var1: num 1 1 $ var2: logi TRUE FALSE $ var3: logi NA NA first result > str(ds) 'data.frame': 2 obs. of 6 variables: $ var1 : num 1 1 $ var2 : logi TRUE FALSE $ var3 : logi NA NA $ value_and_logical: logi TRUE TRUE $ logical_and_na : logi TRUE NA

The quirk as in table(1:3, exclude = 1, useNA = "ifany") is actually somewhat documented, and still in R devel r71104. In R help on 'table', in "Details" section: It is best to supply factors rather than rely on coercion. In particular, ?exclude? will be used in coercion to a factor, and so values (not levels) which appear in ?exclude? before coercion will be mapped to