The crosstable package give me exactly what I need to do some exploratory work in a data set composed of answers to a survey. But I need to weight the crosstabulation to get a representative results of the population I'm studying. Any ideas how I could use weights with this package?
So far I have used the "survey" package to do that, but it's lacking presentation tool to get publication ready tables.
Thanks.
I'm the dev of the crosstable package and it is unfortunately not supporting weights yet.
I would love to implement this as a feature one day, so you should definitely open a Feature Request on GitHub.
As I've never had to do a weighted description myself, please add a simplified version of your use case so that I can make something useful to everyone.
Related
I'm currently dealing with multivariate dynamic time warping (DTW) in R. The best library I found so far is the dtw package as described here: http://dtw.r-forge.r-project.org/
But I do not know how multivariate dtw is actually implemented and it is also not described in the description of the package. All in all, I would like to know if it implements DTWD (Dependent DTW) or DTWI (Independent DTW).
Does anyone have an idea or a suggestion how to find out which of these two approaches the package uses? Or are there libaries which allow me to choose the variant?
You can always look at the companion papers that come with the packages for a more theoretical grounding of the packages
https://www.jstatsoft.org/article/view/v031i07
Additionally, you can always look into the "guts" of the functions to understand how things are implemented in the code
https://www.rdocumentation.org/packages/dtw/versions/1.20-1/source
As far as this question in general, perhaps it is better suited for https://stats.stackexchange.com/ where the questions are more methodological than programming.
The two types of multivariate dynamic time warping are carefully explained here
https://www.cs.ucr.edu/~eamonn/Multi-Dimensional_DTW_Journal.pdf
I want to follow the tutorial found on this site, but despite being thorough in all other aspects, the author has not included information on what packages need to be used for the code to function.
As far as I understand one of them will be the PerformanceAnalytics package, yet my inexperienced eye is not sure about what else I will need to include.
The fapply function used in the code is one example that I cannot find.
fapply()
Error: could not find function "fapply"
library(sos)
findFn("fapply", sortby = "Function")
The findFN(...) function is great. It should open an internet browser window with the search results by itself at least it does for me.
The tutorial on Backtesting a Trading Strategy uses time series data as seen its part 1 and part 2. fapply is also used in part 2
As the data being collected and processed is time-series data, fapply() function belongs to far package which is used for Modelization for Functional AutoRegressive Processes.
I hope this helps.
I am relatively new at using R. I have a dataset of around 5000 datapoints.
My goal is to predict a category using the comments entered.
I have a training dataset of 4500 records and a testing data set of 500 records.
I am looking for 2-3 packages which might help me in doing this.I have to evaluate these packages and prepare a report on that. Can anyone suggest me some good packages which might easier to use and also more efficient.
Again, I have 2 columns
1st one is comments and based on this I have to predict the category.
Right now I have defined around 10 independent categories.
Most of the comments have specific keywords which I have defined as categories
One such example
Comment 1
The website is pretty good --->> category would be WebsiteContent
comment 2 might be like
Excellent article ,very detailed--->> same category as above(WebsiteContent)
But the keywords such as article, website are very limited and can be linked to the category
all of comments are different but the underlying keywords are mostly the same
Thanks,
Ankan
Although all you need is a very long and well written set of if-else statements, try using a Decision tree from the package from the rpart and prp package. I'm saying this only cause you're trying to learn and I'm guessing this is for some assignment which you're supposed to be doing on your own.
tree<-rpart(train$decision~train$comment, method"class")
prp(tree)
The first line builds the model and the second one plots it. This might be a bit overboard actually but since you're learning R this is a fun thing to work with and can be used for a wide variety of things. Although, Decision trees work better with more predictor variables.
Use predict(test,tree) to test out the model on your test dataset.
I was wondering if anyone out there has found a nice package for R to analyse eye-tracking data?
I came across eyetrackR but as far as I can tell there is no English support documentation available:
http://read.psych.uni-potsdam.de/pmr2/index.php?option=com_content&view=article&id=43:eyetrackr&catid=13:r-playground&Itemid=15
I will move onto another freeware that handles eye-tracking data if I need to but was really hoping there would be something accessible in R.
Ideas?
Cheers.
It would help if you could explain which kind of analyses you are intending to do. There are many different approaches depending on the research question and the research field. Many approaches involve the detection of fixations and saccades as a first step. An R package that can be used for fixation detection is called saccades and is available on CRAN. See also the Github page of the package for examples and screenshots.
A new eye-tracking analysis package for R (eyetrackingR) was recently released. It provides a variety of methods that handle data preparation/cleaning, visualization, and analysis.
Here's a list of several dozen instances of researcher contributed code (FOSS) for post-acquisition summarization and analysis of eye-movement data. You may be able to find something to suit your needs there.
List is provided in case anyone stumbling across this thread may find it useful.
https://github.com/davebraze/FDBeye/wiki/Researcher-Contributed-Eye-Tracking-Tools
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.