similar to: Creating Movies with R

Dear All, I am trying to use the do.call command to avoid tedious loops in R when I need to call repetitively a function. I am experiencing a problem when the arguments of a function are given with a vector (I need to express then this way to be able later on to integrate them numerically with the adapt package). Consider the small script: remove(list=ls()) #library(adapt) # first a list of the

Well, the benefit lies in the ability to pass along arguments via `...` to more than one recipient that use *identical argument names* and/or when these recipients are not necessarily located on the same calling stack layer. I'm *not* after a *general* change in the way arguments are dispatched/functions are called as I'm actually a big friend of keepings things quite explicit (thus

I'm making two plots, one on top of the other. On the upper plot, I do not print the x-label or the x-tick-label. To reduce space, I'd like to keep the white space between the two figures at a minimum. However, I can't figure out how to methodically reduce the space while maintaining the same figure dimensions for both plots. I could add margin space below the lower plot and reduce

Hi Gabriel, and thanks for answering. I'm basically just trying to find a way to use the power of `...` in more complex scenarios and I'm well aware that this might not be the best approach ;-) Regarding your actual question: "Are you suggesting methods be dispatched based on the *contents* of ... [...]?" Yes, I guess currently I kind of do - but not on the argument *names*

Hi everyone, I have dataset which I make a sample of it couple of times and each time I get the mean and standard deviation of each row for each sample. I have a function for that, which takes the name of the file and number of times to sample and then returns the mean and standard deviation for each row in each sample. Sample=function(name, n){

I am using a binomial mixture model to estimate abundance (N) and detection probability (p) using simulated count data: -Each site has a simulated abundance that follow a Poisson distribution with lambda = 5 -There are 200 simulated sampled sites -3 repeated counts at each site - only 50 percent of the animals are counted during each count (i.e, detection probability p =0.5, see codes) We removed

Hi List, I want to read several parameters from data frame and load them as object into R session, Is there any package or function in R for this?? Here is example param <-c("clust_num", "minsamp_size", "maxsamp_size", "min_pct", "max_pct") value <-c(15, 20000, 200000, 0.001, .999) data <- data.frame ( cbind(param , value)) data

I'm not aware of any routine that those the job, although I think that it could be relatively easily done by multiplication the manifest variable vector with the estimates for the specific effect. To make an example: v1; v2; v3; v4 are manifest variables that loads on one y latent variablein a data frame called "A" the code for the model should be like: model <-specifymodel( y

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

On Wed, Oct 12, 2016 at 10:53 AM, Hal Finkel <hfinkel at anl.gov> wrote: > I don't think that Clang/LLVM uses it by default on x86_64. If you're using -Ofast, however, that would explain it. I recommend looking at -O3 vs -O0 and make sure those are the same. -Ofast enables -ffast-math, which can legitimately cause differences. > The following tests pass at "-O3" and

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence

Hello, everybody: I'm back to share a long piece of example code. Because I had trouble understanding the role of par() and margins when customizing barplots, I have struggled with this example. In case you are a student wanting to place legends or textual markers in the outer regions of a barplot, you might run these examples. There are a few little things I don't quite understand.

================================================= Mon-03-31-2008, 11:04pm U.S.EDT Hello, I''m trying to get a website entry page to work properly, while learning Scriptaculous effects and javascript at the same time. I wanted to use a couple of effects to add a little flash-style animation. I managed to get the initial Appear to work but with a problem: the image that''s supposed

Hi all, As requested by at least one person (and because I''d been thinking about it and figured it would be a good idea), I''ve just made it simple to use external node sources with Puppet. If you set ''external_nodes'' to a command (the default is ''none''), then Puppet will call that command and use it to configure nodes. The output of