I found a package 'bivpois' for R which evaluates a model for two related poisson processes (for example, the number of goals by the home and the away team in a soccer game). However, this package seems to no longer be useable in newer versions of R.
Is there a reasonable way to modify the glm() function to do a similar process, or run this older package on my new version of R? I have found very little literature on these sorts of processes and have found very little in terms of easy implementation in other statistical packages like STATA.
Any suggestions would be much appreciated.
While CRAN does not host a current binary of bivpois, you can build the package from the archived source code (see http://cran.r-project.org/doc/manuals/R-exts.html#Checking-and-building-packages ). Building bivpois 0.50-3.1 from source (available at http://cran.r-project.org/src/contrib/Archive/bivpois/) works for me on R 2.15.0 Windows x64. The zipped Windows binary I built is available here: http://commondatastorage.googleapis.com/jthetzel-public/bivpois_0.50-3.1.zip .
You can feel free to refer to odds modelling and testing inefficiency of sports-bookmakersas I had modified the relevant functions inside bivpois package.
Related
Installed both R studio and ConsRank package but i have little R and Math knowledge.
I need to understand how to calculate and reach the result of the formula in attached files.
To solve this problem, correlation coefficient τx used which is intruduced by Emond and Mason (2002)
I tried to use ConsRank functions but my R knowledge was not enough.
FormulaFormula
MatrixMatrix
ResultResult
The first step is to install and load the package ConsRank using the following codes:
install.packages('ConsRank', dependencies=T)
library(ConsRank)
If you successfully load the package, you are good to go. The next step is to read the documentation of the package (Click Here) in which you will learn about what functions you will need and there are some different examples that help you understand the input and outputs.
Let me know if you needed any help running your scripts. Good Luck.
I know one of the reasons R is so popular is because of its amazing packages. But for data security reasons, I can't install packages on my work computer. So, it got me thinking if I could still make R do what I would typically make it do using packages with just base R, since packages are, after all, a compiled list of functions. I am wondering if it is possible run regression models and make charts in base R (without using, say ggplot2, caret, etc.). Is it possible to copy the functions in these packages into base R to get the same functionality out of base R as one would if they were using the packages? Is the list of functions that are published as part of these packages available somewhere publicly by chance?
I am wondering if it is possible run regression models and make charts in base R (without using, say ggplot2, caret, etc.).
Yes, before ggplot2 was invented, R was genereally praised for publication ready graphics. R comes with great plotting capabilities without ggplot2 even though the latter is definitively an improvement.
Obviously, people used R for regression decades before caret was invented. A base R installation comes with a solid set of linear and nonlinear regression methods but obviously, all those packages (well, most of them) have a reason to exist. It will mainly depend on what you plan to do use. Many things are implemented in a base installation, many are not.
You can find lists of packages included with all binary distributions of R here: https://cran.r-project.org/doc/manuals/r-release/R-FAQ.html#Add_002don-packages-in-R
You will find, that that not only includes the stats package but lots of useful modelling packages like MASS, splines, boot, mgcv, nlme, cluster, rpart, spatial and survival, so a large number of even specialized models is at hand without additional downloading of packages.
Is it possible to copy the functions in these packages into base R to get the same functionality out of base R as one would if they were using the packages?
Many packages contain just plain R code, others will contain code in other languages, mostly C and C++, which will need a compiler to be translated on your system. However, where the use of foreign code / packages is considered a security breach, you should refrain from that and talk to your employer.
If it is not considered a problem but they do not want to make exceptions for you and your installation -- I was in the same place for quite some time and I just ran R from a USB stick. If that is allowed and feasible on your system, you can download packages to that USB stick installation.
I have been using Python to fit an ARCH model to monthly return series of Intel stock from 1989-2010. I have used the ARCH library written by Kevin Shepphard. Now, when cross checking with R, my coefficients of Volatilty model is slightly different than what R tells me it is. I am wondering, why is there so many differences in results across packages? Which language is correct then? R's fGarch package or Kevin shepphards package? The problem is the p values across the two languages are completely different. I'm confused which language to use to get the correct results. I have attached the link to my work below. If you scroll down, you will be able to see my Python implementation where I'm trying to fit a arch(3) model and likewise Rs implementation. If someone can please explain where the difference is coming from and which package to trust, I would highly appreciate it
Thanks
http://nbviewer.ipython.org/gist/mrajancsr/96a19065794c8c0bd850
Fixed in 95ccc3e on August 6, 2015
https://github.com/bashtage/arch/commit/95ccc3e94d408d92c6d0d8635a62ff2a26243f45
Can I use pairwise.wilcox.test() for post hoc test as my friedman.test() gat sifnificant?
I can't install pgirmass for the friedmanmc() function as its not compatible with my R version.
Does pairwise.wilcox.test() make sense for more than two samples?
Thank you for your help!
You haven't offered a specific example or an explanation of the the study design and hypotheses being tested, but the documentation does say that "corrections for multiple testing" are made, so you should be reasonably safe on statistical grounds. (There is some debate about the need for multiple comparisons tests.)
On the topic of the other package, you are misspelling its name and there is a current version available from CRAN for pkg:pgirmess. After reading the documentation of the two tests, I would probably trust the pairwise.wilcox.test more than the friedmanmc test because it is in a core R package, while the friedmanmc test appears to have undesireable behavior that gets suppressed in an awkward fashion, leading me to think it uses something of a statistical hack. I'm not encouraging you to do so but if your unstated R version is somewhat older, there may be suitable package versions, since I see versions going back to 2005 in the Archives.
I'm looking for a an R package which can be used to train a Dirichlet prior from counts data. I'm asking for a colleague who's using R, and don't use it myself, so I'm not too sure how to look for packages. It's a bit hard to search for, because "R" is such a nonspecific search string. There doesn't seem to be anything on CRAN, but are there any other places to look?
I've only come across both R and the Dirichlet distribution in passing, so I hope I'm not too much off the mark.
This mailing list message seems to answer your question:
Scrolling through the results of
RSiteSearch("dirichlet") suggests some useful tools
in the VGAM package. The gtools package and
MCMC packages also have ddirichlet() functions
that you could use to construct a (negative log) likelihood
function and optimize with optim/nlmin/etc.
The deal, DPpackage and mix packages also may or may not provide what you need.
Then again, these are all still CRAN packages, so I'm not sure if you already found these and found them unsuitable.
As for searching for R, the R project site itself already provides a few links on its search page.