Most commonly used statistical tests and implementation in R • Hacker News Board

Re: Most commonly used statistical tests and implementation in R

#31

Post by minimaxir » Fri, Jan 01, 2016, 6:52 PM UTC

stared wrote: post #21

minimaxir wrote: post #5

Earlier quoted context omitted.

> Jupyter[2] notebooks now have really first class support for R. Jupyter and R is a bit iffy since the R kernel is not native. Although the kernel works fine, setting it up has a ton of manually-installed dependencies, and in-line plots flat-out give unexpected output. (I've had to cheat by embeding charts via Markdown. Although that has the benefit of having the charts be responsive) The important perk is that Jupy…

Manually setting it is hard (on OS X + Homebrew Python I did it after a long fight; main problem: rmzq library). But... it is super easy with Anaconda: https://www.continuum.io/blog/developer/jupyter-and-conda-r

Huh, I thought conda was Python only. I'll definitely take a look!

Re: Most commonly used statistical tests and implementation in R

#32

Post by sandGorgon » Fri, Jan 01, 2016, 7:17 PM UTC

earino wrote: post #30

sandGorgon wrote: post #15

Earlier quoted context omitted.

Interesting. Did you post about this on /r/rprogramming There are lots of people who have rolled their own solutions for production deployment. Including nodejs !

Greetings! So I made a small demo for you. If you go to https://app.dominodatalab.com/earino/d3_dashboard_demo/raw/6... you will see a very simplistic, d3 powered, R backed dashboard. It just draws a pie chart, a line chart, and a bar chart. Every 10 seconds, it polls an R API endpoint to get new data. The code for the R endpoint is https://app.dominodatalab.com/earino/d3_dashboard_demo/view/... . It's a simple R fun…

this is awesome! you should put stuff like this on the front page of your site. It makes it incredibly simple to understand what you are about.

Re: Most commonly used statistical tests and implementation in R

#33

Post by healer » Fri, Jan 01, 2016, 9:12 PM UTC

cetacea wrote: post #29

ekianjo wrote: post #16

> If the p-Value is less than significance level (ideally 0.05), Erm, no. P=0.05 is borderline meaningless, there could as much as 30% chance you are wrong about the actual difference being there depending on the true probability of the initial hypothesis. P-values should be used with strong caution.

Even better, p-values should not be used at all. If I have data in hand, I want to use it to find out the probability that my hypothesis is true. But p-value analysis requires me to instead ask a different question that I don't really care about, involving whether my data are consistent with the null hypothesis. Everything is just so much more sensible if you allow yourself to assign probabilities to hypotheses, rath…

There is in fact a probability attached to p-values. A p-value of 0.05 for instance means your conclusions will be wrong 5 out of 100 times. You can reduce the p-value to e.g. 0.001 or any other value you want.

Re: Most commonly used statistical tests and implementation in R

#34

Post by cwyers » Fri, Jan 01, 2016, 10:13 PM UTC

healer wrote: post #33

cetacea wrote: post #29

Earlier quoted context omitted.

Even better, p-values should not be used at all. If I have data in hand, I want to use it to find out the probability that my hypothesis is true. But p-value analysis requires me to instead ask a different question that I don't really care about, involving whether my data are consistent with the null hypothesis. Everything is just so much more sensible if you allow yourself to assign probabilities to hypotheses, rath…

There is in fact a probability attached to p-values. A p-value of 0.05 for instance means your conclusions will be wrong 5 out of 100 times. You can reduce the p-value to e.g. 0.001 or any other value you want.

No, it means that the probability of seeing an effect of that magnitude on a dataset of that size when the null hypothesis is true will happen due to random chance 5 out of 100 times. It says NOTHING about your hypothesis, it is entirely a statement about the null hypothesis.