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You not only need to install a package, but you need to load it for a particular script. That's what require('rJSON') is doing. Similarly, you could use library(rJSON) at the beginning for similar ef …

For a single binary exposure like what you are describing, which one R returns doesn't really change things. …

Assuming, as others have, that the small blip below zero is an artifact of a density smoothing process, rather than a small amount of negative data, your distribution looks like an exponential distrib …

Sadly this won't do you much good in R, but since I think it's faintly on topic for the general question, this link on my blog: http://confounding.net/2010/12/01/randomly-generating-a-truncated-normal-distribution …

As mentioned above, there's "R for SAS Users", and you might also want to consider looking at the "SAS and R" blog: http://sas-and-r.blogspot.com/ and the accompanying book, which provides worked examples … in both SAS and R. …

To be blunt, the plot you posted looks correct to me. Your data set hasn't been "reduced" to 25 variables - you only have values of the data up that far (technically only up to 23, but whose counting? …

Beyond that, SAS is supported by a massive infrastructure of books - the use R! series is helping this in R, but it's not quite there yet. … If R is broken you call....? …

One can find the answer to this by digging into some of the examples: A study was conducted by Feychting et al (1998) comparing cancer occurrence among the blind with occurrence among those who …

R keeps all its data in memory, which means as you start having huge data sets - or large collections of data sets - you're going to top out your RAM. … If you're running out of memory resources, recode your project to save these data sets to a file, clear them from R -using rm() - and then open them back up when you need them. …

I would suggest taking a look at NetworkX in Python, to add to the tremendous list of recommendations you're getting. I've found it to be quite flexible. It expressly allows things like images to be n …

I'd argue your question isn't really that heavily dependent on the R language, and more appropriate here, because - to be blunt - the generation of data like this is mostly a statistical task, rather than …

I'm not fluent enough with R to suggest a language-specific approach for R. … It shouldn't be too hard to adapt the coding to R. http://www.nesug.org/Proceedings/nesug09/ph/ph12.pdf …

There are several R software packages one could use, including "sna" and "network". One thing I wouldn't necessarily rely on if you're having performance issues with sna is NetworkX. …

Well, that was...fairly easy. Inspired by an unrelated graph in Visualize This: plot(data$t2, type="h", col="grey", lwd=2, xlab="Subject", ylab="Days Since Start") lines(data$t1, type="h", col="l …

So by "absolute risk" I'm going to assume you mean either the cumulative probability of an event at time t, or the hazard at time t. In short, no, a Cox proportional hazards model doesn't really give …