similar to: simple apply syntax

I have a vector of probabilities that I would like to feed to rbinom() and have it return a 1 or 0 for each element. I have tried simply using the entire vector as the argument hoping R would understand. Instead I get a scalar: theProbs <- runif(10,0,1) youreON <- rbinom(1, 1, theProbs) Next I tried using the apply function but I could not get that to work either: youreON2 <-

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.

Simply put, I want to subset the data frame 'a' where 'y=0'. > a <- as.data.frame(cbind(x=1:10, y=c(1,0,NA,1,0,NA,NA,1,1,0))) > a x y 1 1 1 2 2 0 3 3 NA 4 4 1 5 5 0 6 6 NA 7 7 NA 8 8 1 9 9 1 10 10 0 > names(a) [1] "x" "y" > table(a$y) 0 1 3 4 > table(a$y, useNA="always") 0 1 <NA> 3 4

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

Hi list! I have prepared a custom function (below) in order to calculate separability indices (Divergence, Bhattacharyya, Jeffries-Matusita, Transformed divergene) between two samples of (spectral land cover) classes. I need help to cross-compare results to verify that it works as expected (since I don't know of any other foss-tool that will give me quickly some results). Does anybody

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)

Hi, I have a somewhat complicated relationship question. I''ve been banging my head about this for some time and have given in and decided to ask the list :) Here''s what I''m trying to achieve: Products can have variants. For example, a t-shirt could come in small, medium and large, but also black, green and blue in the first two sizes, but only blue in large.

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

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

??? -- You received this message because you are subscribed to the Google Groups "Ruby on Rails: Talk" group. To post to this group, send email to rubyonrails-talk-/JYPxA39Uh5TLH3MbocFF+G/Ez6ZCGd0@public.gmane.org To unsubscribe from this group, send email to rubyonrails-talk+unsubscribe-/JYPxA39Uh5TLH3MbocFF+G/Ez6ZCGd0@public.gmane.org For more options, visit this group at

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.

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 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 can use gdata to successfully read in the example Excel file, but not any other excel files. Why might this be the case? It seems that the problem has something to do with opening the database but no indication as to what the problem is. So i'm at a loss of how to fix it. > library(gdata) gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED. gdata: read.xls support

Hello folks, I am currently working on a project that has to deliver an admin interface like the Django''s one, I wanna know if you know/ recommend any gem or plugin for that. Cheers, Rodrigo Alves Vieira -- You received this message because you are subscribed to the Google Groups "Ruby on Rails: Talk" group. To post to this group, send email to

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, 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

Hi, I have a dataframe containing data from individuals 1, ..., 12 (grouping variable "g" in the data frame below), which belong either to "A" or "B" (grouping variable "f"): set.seed(1) tmp <- data.frame(

Are these two below the same thing? 1. module X module Y class Z 2. class X::Y::Z -- You received this message because you are subscribed to the Google Groups "Ruby on Rails: Talk" group. To post to this group, send email to rubyonrails-talk-/JYPxA39Uh5TLH3MbocFF+G/Ez6ZCGd0@public.gmane.org To unsubscribe from this group, send email to