Articles by M. Parzakonis

R 2.11.0 just landed…

April 23, 2010 | 0 Comments

The new version is here. R version 2.11.0 has been released on 2010-04-22. The source code is first available in this directory, and eventually via all of CRAN. Binaries will arrive in due course (see download instructions above). [Read more...]

Poor man’s pairs trading…

April 11, 2010 | 0 Comments

There is a central notion in Time Series Econometrics, cointegration. Loosely it refers to finding the long run equilibrium of two non-stationary series. As the most know non-stationary series examples comes from finance, cointegration is nowadays a tool for traders (not a common one though!). They use it as the ...
[Read more...]

A von Mises variate…

March 25, 2010 | 0 Comments

Inspired from a mail that came along the previous random generation post the following question rised : How to draw random variates from the Von Mises distribution? First of all let’s check the pdf of the probability rule, it is , for . Ok, I admit that Bessels functions can be a ...
[Read more...]

R 2.11.0 due date

March 23, 2010 | 0 Comments

This is the announcement as posted in the mailing list : This is to announce that we plan to release R version 2.11.0 on Thursday, April 22, 2010. Those directly involved should review the generic schedule at http://developer.r-project.org/release-checklist.html The source tarballs will be made available daily (barring build troubles) ... [Read more...]

The distribution of rho…

March 21, 2010 | 0 Comments

There was a post here about obtaining non-standard p-values for testing the correlation coefficient. The R-library SuppDists deals with this problem efficiently. library(SuppDists) plot(function(x)dPearson(x,N=23,rho=0.7),-1,1,ylim=c(0,10),ylab="density") plot(function(x)dPearson(x,N=23,rho=0),-1,1,add=TRUE,col="steelblue") plot(function(... [Read more...]

In search of a random gamma variate…

March 16, 2010 | 0 Comments

One of the most common exersices given to Statistical Computing,Simulation or relevant classes is the generation of random numbers from a gamma distribution. At first this might seem straightforward in terms of the lifesaving relation that exponential and gamma random variables share. So, it’s easy to get a ...
[Read more...]

\pi day!

March 14, 2010 | 0 Comments

It’s π-day today so we gonna have a little fun today with Buffon’s needle and of course R. A well known approximation to the value of $latex \pi$ is the experiment tha Buffon performed using a needle of length,$latex l$. What I do in the next is ... [Read more...]

In a nls star things might be different than the lm planet…

March 10, 2010 | 0 Comments

The nls() function has a well documented (and discussed) different behavior compared to the lm()’s. Specifically you can’t just put an indexed column from a data frame as an input or output of the model. __ nls(data[,2] ~ c + expFct(data[,4],beta), data = time.data, + start = start.list) Error ... [Read more...]

PoRtable…

February 24, 2010 | 0 Comments

Jobless as I might be, I do have some clients for data analysis. I try not to visit them in their office coz then things get really slow and time-consuming. When I can’t escape this, the worst thing is tuning data and software with client. So, I have a ...
[Read more...]

A quicky..

February 22, 2010 | 0 Comments

If you’re (and you should) interested in principal components then take a good look at this. The linked post will take you by hand to do everything from scratch. If you’re not in the mood then the dollowing R functions will help you. An example. # Generates sample matrix ... [Read more...]

The truncated Poisson

February 21, 2010 | 0 Comments

A common model for counts data is the Poisson. There are cases however that we only record positive counts, ie there is a truncation of 0. This is the truncated Poisson model. To study this model we only need the total counts and the sample size. This comes from the sufficient ...
[Read more...]

Uh!

February 20, 2010 | 0 Comments

Didn't know this... a data 0 2 4 7+ 25 34 12 5 It's becoming clear that I have learned R in the most unstructured way...I always do it in two stages :ashamed: [Read more...]

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)