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Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.

0
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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 …
answered Jul 2 '14 by Fomite
7
votes
I've been in your shoes - indeed am probably still in your shoes - as I use both R and SAS regularly for different tasks. 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. Generally speaking, the experience in switching …
answered Sep 16 '11 by Fomite
0
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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 …
answered Sep 2 '11 by Fomite
2
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these in broadly the same way for interpretation. For a single binary exposure like what you are describing, which one R returns doesn't really change things. The HR for Radiation=No is just 1/0.5882 …
answered Jun 15 '17 by Fomite
1
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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 …
answered Aug 22 '11 by Fomite
1
vote
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? …
answered Oct 12 '11 by Fomite
1
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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 …
answered Sep 30 '15 by Fomite
6
votes
, simulations that can be run on computers with megabytes of available RAM - and one which crushed a cluster node with 96 GB of memory. R keeps all its data in memory, which means as you start having huge data … 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. This will slow down your code somewhat, but is way cheaper than buying new RAM, especially as a Macbook Pro is going to top out at 16 GB anyway. …
answered Sep 3 '11 by Fomite
41
votes
So I use both R and SAS - admittedly in academia - but there are a couple reasons that I tend to head toward SAS at times: Better documentation. R is getting better at this, but documentation … , especially the official documentation, is often kind of terrible and opaque. 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 …
answered Aug 11 '12 by Fomite
7
votes
1answer
I'm analyzing the results of some simulation work using a Cox proportional hazard model, and I have what I perceive are a great many ties in the data, representing when a particular individual in the …
asked Jul 18 '12 by Fomite
4
votes
3answers
Using either truehist() from MASS or just the normal hist() function in R with the prob=TRUE option, I'm getting very strange values for the y-axis. I was under the impression that these values …
asked Oct 19 '11 by Fomite
3
votes
1answer
-up. Does anyone know a way in either R or Stata to either change the size of the points plotted - so you essentially have a funnel-bubble plot, or to color them on a continuous gradient? Clearly …
asked Sep 12 '11 by Fomite
4
votes
1answer
RR ~ OR relationship doesn't hold. I've implemented a model in R to do that, as follows: uni.out <- glm(Death ~ onset, family= binomial(link=log), data=data) But I'm continually getting … the posterior (this is being used alongside multiple imputation, so I can't just report the posterior). The problem is, I have no idea how to implement either one of these in R, nor if they're the …
asked Jul 3 '14 by Fomite
1
vote
The use of normalized weights is done to reduce extreme weights on one end or the other of the spectrum. It's possible that, for your data, no or few such weights occurred, so the normalized weights a …
answered Dec 2 '16 by Fomite
5
votes
You may want to look at these two entries from 'SAS and R': http://sas-and-r.blogspot.com/2011/07/example-91-scatterplots-with-binning.html http://sas-and-r.blogspot.com/2011/07/example-92 …
answered Mar 10 '12 by Fomite

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