similar to: Why does my RPy2 program run faster on Windows?

Dear list, When calling R from C, what appears like a spurious error can be triggered during the execution of chisq.test(x, y). This is happening when the following conditions are met: - x and y are "anonymous" C-level R vectors (they do not have a symbol), but they are protected from garbage collection - x and y are "not too small" (it was experienced as soon as they are

All, I'm attempting to run this: table.CAPM(series[,"Strat.Return",drop=FALSE],series[,"spy.Return",drop=FALSE]) and getting this error Error in as.vector(data[, i]) : subscript out of bounds I've searched around and cannot find a solution to the problem.  I've used this in the past without problem and I'm not sure what I did different this time. Other

I am trying to create a pie chart using rpy2 of the following data Pathway Occurrences A 10% B 50% C 20% E 10% filename = './testing.png' grdevices = importr('grDevices') rdevices.png(file=filename, width=1200, height=800) dataf = Robjects.DataFrame({'pathways':Robjects.StrVector(Pathways),

Hi all, I am new to python, R and rpy2. I having few errors when I am using 'fisher.test' function where I am struck now. Please help to fix these bugs. My code for fisher.test goes this way *from rpy2 import * import rpy2.robjects as robjects def fisherExact(a,b,c,d): v = [a,b,c,d] m = robjects.r['matrix'](v,2,2) s = robjects.r['fisher.test'](m) return s *

Hi all, I am also looking for interesting statistical experiments about testing and estimating CAPM, APT, Fama models, etc. using R using financial series data... please give me some pointers... I have been searching the R archives for the past a few hours and I vaguely got to know that there are programs do these interesting statistical things, but I just could not find where are they... I have

Hey Guys, i want to do a CAPM-GARCH model. I didn?t find anything posted online. (If there is something - shame on me - i didn?t find it.) My Problem: What is the difference if I let the residuals ?e? follow a garch process ? How do I do my regression analysis now? I began reading about regression analyis with heteroscedasticity, but didn?t get it. So i started programming. First

Dear list, there seems to be a problem with the standard location of R?s 64-bit libs at least under openSUSE. I had two user questions regarding the compilation of rpy2 for 64-bit systems. Personally I don`t know rpy2 but maybe someone on this list can decide if the mistake lies on R`s side or on rpy2?s side or in between. R-devel is installed in these cases, I asked. Latest R-base or

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Hi all ARCH modelling I have a problem now on how to proceed with further steps in my analysis. I did a linear OLS regression with my daily data of stock and index returns. There is now the problem of arch in my error terms. Thus I used the following r command: garch(resid_desn, order=c(0,2)) ## This ARCH(2) process seems to fit the best after trial and error. Consequently, I get there three

Beside the possible argumentation that with an API elegance and convenience might sometimes be superior to necessity, the suggested pattern ("every program, including R itself, keeping its own flag") does no work too well when the nested embedding of R is involved. A concrete example is: ``` $ R -q > library('rPython'); python.exec('import rpy2.robjects') R is already

Full_Name: Anjali Shitole Version: R 2.5.1 OS: Submission from: (NULL) (202.141.157.91) I want to compute coefficient of variation for each subset of data. Each subset is heterogeneous means number of rows in each subset are different.These all subsets are in one dataset situated one by one. i have used aggregate command to calculate mean for each subset. But same command for calculating sd

Dear all, I want to do piecewise CAPM linear regression in R: RRiskArb−Rf = (1−δ)[αMktLow+βMktLow(RMkt−Rf)] + δ[αMkt High +βMkt High(RMkt −Rf )] where δ is a dummy variable if the excess return on the value-weighted CRSP index is above a threshold level and zero otherwise. and at the same time add the restriction: αMkt Low + βMkt Low · Threshold = αMkt High + βMkt High · Threshold to ensure

Inthe code below I was trying to to obtain the GMM estimates for CAPM (REGRESSION) for 36 stocks each have 180 observations,however it only gives me one output rather than 36. In SAS i would just put in a *By statement*. I have a variable TICKER that categorize them into 36 groups. *How can I obtain all 36 output instead of just one.* **

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On 20 October 2010 at 09:57, Aman Thakral wrote: | Thanks for your reply. I just created a link to | /usr/local/lib/R/lib/libRlapack.so in /usr/local/lib. It works now. | I'm just curious, is this the "right" way to do it? Is there a better way? I am coming to this a little late but ... it is a band aid to a self-inflicted wound. You only have libRlapack because you built R

rPython appears to provide an interface from R to Python by embedding Python and I'd think that it can safely assume that R has been initialized, but might not be the point here. The issue is that a Python package embedding itself R (here rpy2) appears to have no way to know that earlier in the life of the process R was initialized. 2015-05-03 19:48 GMT-04:00 Duncan Murdoch

In Python, it is literally this easy: import rpy2.robjects as robjects robjects.r(""" source("C:/ YOUR R FILE GOES HERE ") """) Type the name of your R source code into this script and save it as a Python script (add the suffix .py), and then you can run by double-clicking. If you want to see the results of your program, you'll have

Hi all I am looking for an add-in. I am currently working on something and I use daily data of closing stock prices. As not all companies are traded daily (e.g. on monday, then on thursday etc) at the stock exchange, there is satistically a problem. There are some papers which explain the approach to handle infrequent trading of a stock or non synchronous data and beta estimation (Dimson, 1979;

Hi again. I'm putting together a little project with R, python, and a website. So I have an HTML file, a py file, an R file. Here is the HTML file: <form action="/cgi-bin/radio4.py" method="post" target="_blank"> <input type="radio" name="subject" value="Integrate" /> Integrate <input type="radio"

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