Stats raving mad

The blog

Tag Archives: R

The distribution of rho…

by M. Parzakonis on March 21, 2010

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(x)dPearson(x,N=23,rho=-.2),-1,1,add=TRUE,col=”green”) plot(function(x)dPearson(x,N=23,rho=.9),-1,1,add=TRUE,col=”red”);grid() legend(“topleft”, col=c(“black”,”steelblue”,”red”,”green”),lty=1, legend=c(“rho=0.7″,”rho=0″,”rho=-.2″,”rho=.9″))</pre> This is how it looks like, Now, let’s construct a table of critical values for some arbitrary or not significance levels. q=c(.025,.05,.075,.1,.15,.2) xtabs(qPearson(p=q, N=23, rho [...]

In search of a random gamma variate…

by M. Parzakonis on March 16, 2010

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 gamma random variate using the fact that [...]

\pi day!

by M. Parzakonis on March 14, 2010

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 only to copy from the following file the function [...]

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

by M. Parzakonis on March 10, 2010

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 in parse(text = x) : unexpected [...]

PoRtable…

by M. Parzakonis on February 24, 2010

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 USB with portable versions of my [...]

A quicky..

by M. Parzakonis on February 23, 2010

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 of five discrete clusters that have [...]

The truncated Poisson

by M. Parzakonis on February 22, 2010

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 statistic principle as the [...]

lme4 stands 4 Linear mixed-effects…

by M. Parzakonis on February 20, 2010

There is a certain hype about mixed (and random) effects among statistician and analysts. You can show some love to Douglas Bates and Martin Maechler for maintaing the lme4 package for our cupid, R I copy the entity of the information of the projects page. Doxygen documentation of the underlying C functions is here. The [...]