## It’s time,huh?

Well, i managed to take my personal webspace somewhere out there and of course the first thing to set up was my blog ;). WordPress, was great but there was limitations you know. At this point I’d like to present you my new blog address, statsravingmad.com/blog. I hope y’all follow me there. Please update you bookmarks…

CU!

## In church…

## A von Mises variate…

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 bit frightening, but there is a work around we can do. The solution is a Metropolis algorithm simulation. It is not necessary to know the normalizing constant, because it will cancel in the computation of the ratio. The following code is adapted from James Gentle’s notes on Mathematical Statistics .

n <- 1000 x <- rep(NA,n) a <-1 c <-3 yi <-3 j <-0 i<-2 while (i < n) { i<-i+1 yip1 <- yi + 2*a*runif(1)- 1 if (yip1 < pi & yip1 > - pi) { if (exp(c*(cos(yip1)-cos(yi))) > runif(1)) yi <- yip1 else yi <- x[i-1] x[i] <- yip1 } } hist(x,probability=TRUE,fg = gray(0.7), bty="7") lines(density(x,na.rm=TRUE),col="red",lwd=2)

## R 2.11.0 due date

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) via http://cran.r-project.org/src/base-prerelease/ For the R Core Team Peter Dalgaard

## The distribution of rho…

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 = 0, lower.tail = FALSE, log.p = FALSE) ~ q )
# q
# 0.025 0.05 0.075 0.1 0.15 0.2
# 0.4130710 0.3514298 0.3099236 0.2773518 0.2258566 0.1842217
```

We can calculate p-values as usual too…

```
1-pPearson(.41307,N=23,rho=0)
# [1] 0.0250003
```

## Oh, mr counselor!

Xm…

There were two men trying to decide what to do for a living. They went to see a counselor, and he decided that they had good problem solving skills.

He tried a test to narrow the area of specialty. He put each man in a room with a stove, a table, and a pot of water on the table. He said “Boil the water”. Both men moved the pot from the table to the stove and turned on the burner to boil the water. Next, he put them into a room with a stove, a table, and a pot of water on the floor. Again, he said “Boil the water”. The first man put the pot on the stove and turned on the burner. The counselor told him to be an Engineer, because he could solve each problem individually. The second man moved the pot from the floor to the table, and then moved the pot from the table to the stove and turned on the burner. The counselor told him to be a mathematician because he reduced the problem to a previously solved problem.

– From The Mathematician, The Physicist and The Engineer (and Others)