I would like to analyze data which is sampled at different frequencies. There is a concept developed by Ghysels called Mixed-data sampling (MIDAS). I tried to find some functions or packages in R applying this method, but could't find anything. Does anybody know any function or package in R for this concept?
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I've tried to search for the answer to this question but have not found any.
Does the CochranArmitageTest() function in R support adjustments for additional variables, apart from the two-level dependent and k-leveled independent variable? How in that case should the x (frequency table or matrix) be presented for the function?
Best regards
That particular test is specifically only written to assess a single ordinal or categorical variable and a binary outcome variable. It is not, as far as I know modifiable.
However, it is my understanding that the Bioconductor project, which curates a lot of pharmaceutical, biological and genetics packages in R, hosts a package developed about 5-years ago to work with multiple categorial or ordinal variables and binary outcome.
It is in the globaltest package which you can install with the following directy from the Bioconductor repository.
BiocManager::install("globaltest")
Here is the PDF explaining the whole package:
This is a short question.
Is there a way to perform abstractive text summarization in R?
There are ways to perform extractive summary (ie extract few relevant sentences), as shown here Text summarization in R language
However, I was unable to find a way to do abstractive summary "purely" in R. I understand that abstractive summary is way more complex and typically requires model training. The two methods that might work so far are using python through reticulate package or use on of google's APIs. was wondering if anyone is aware of R package that does that without external dependencies.
thank you in advance
I am looking for an R package which can run "Spatial Vector Autoregression".
tandfonline.com/doi/full/10.1080/17421770701346689
According to Chen and Conley (2001), this is a "vector autoregression (VAR) whose coefficient matrix and shock covariance matrix are functions of economic distances between agents. The impact of other agents’ variables on the conditional mean of a given agent’s variable is a function of their economic distances from this agent. Similarly, covariances of VAR shocks are functions of distances between agents in the previous period, a property we refer to as being isotropic."
(Chen, X & Conley, T.G. (2001) A new semiparametric spatial model for panel
time series, Journal of Econometrics, 105, 59–83)
Surprisingly, however, I could only see until "Spatial Autoregression" which is still not what I need for my purpose. May I get help finding the package for this please? Otherwise, may I know an official way to run this Spatial Vector Autoregression model using R programming?
I think I've found what you're looking for, devtools::install_github("James-Thorson/VAST"). VAST stands for "Vector-Autoregressive Spatio-Temporal." This package is a wrapper around a package that incorporates spatial modeling. Essentially it adds to it.
You can see coding examples here. If you want to look at help, use ?VAST::VAST and select one of the three hyperlinks at the bottom of the short description and details (make_settings, fit_model, and plot_results).
Please note:
When I installed this package to check out what it included, it came back with a conflict that the package TMB required an earlier version of the Matrix package. I had not had TMB installed before installing this package. I had no issues installing TMB independently (without a conflict with the version of the Matrix package). However when I called the library VAST it still gave me that error. When I called the library TMB, then the library VAST I didn't receive the warning and both libraries loaded.
I am looking for ways to determine number of optimal factors in R factanal function. The most used method (conduct a pca and use scree plot to determine the number of factors) is already known to me. I have found a method described here to be easier for non technical folks like me. Unfortunately the R script is no longer accessible in which the method was implemented. I was wondering if there is a package available in R that does the same?
The method was originally proposed in this study: Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure.
The R code is now moved here as per the author.
EFA.dimensions ist also a nice and easy to use package for that
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.