## Why does R allow us to exceed array bounds here?

April 4th, 2014

A colleague recently sent me the following code snippet in R

> a=c(1,2,3,40)
> b=a[1:10]
> b
  1  2  3 40 NA NA NA NA NA NA


The fact that R didn’t issue a warning upset him since exceeding array bounds, as we did when we created b, is usually a programming error.

I’m less concerned and simply file the above away in an area of my memory entitled ‘Odd things to remember about R’ — I find that most programming languages have things that look odd when you encounter them for the first time. With that said, I am curious as to why the designers of R thought that the above behaviour was a good idea.

Does anyone have any insights here?

## Simple nonlinear least squares curve fitting in R

December 6th, 2013

The R code used for this example comes from Barry Rowlingson, so huge thanks to him.

A question I get asked a lot is ‘How can I do nonlinear least squares curve fitting in X?’ where X might be MATLAB, Mathematica or a whole host of alternatives.  Since this is such a common query, I thought I’d write up how to do it for a very simple problem in several systems that I’m interested in

This is the R version. For other versions,see the list below

The problem

xdata = -2,-1.64,-1.33,-0.7,0,0.45,1.2,1.64,2.32,2.9
ydata = 0.699369,0.700462,0.695354,1.03905,1.97389,2.41143,1.91091,0.919576,-0.730975,-1.42001

and you’d like to fit the function using nonlinear least squares.  You’re starting guesses for the parameters are p1=1 and P2=0.2

For now, we are primarily interested in the following results:

• The fit parameters
• Sum of squared residuals
• Parameter confidence intervals

Future updates of these posts will show how to get other results. Let me know what you are most interested in.

Solution in R

# construct the data vectors using c()
xdata = c(-2,-1.64,-1.33,-0.7,0,0.45,1.2,1.64,2.32,2.9)
ydata = c(0.699369,0.700462,0.695354,1.03905,1.97389,2.41143,1.91091,0.919576,-0.730975,-1.42001)

# look at it
plot(xdata,ydata)

# some starting values
p1 = 1
p2 = 0.2

# do the fit
fit = nls(ydata ~ p1*cos(p2*xdata) + p2*sin(p1*xdata), start=list(p1=p1,p2=p2))

# summarise
summary(fit)

This gives

Formula: ydata ~ p1 * cos(p2 * xdata) + p2 * sin(p1 * xdata)

Parameters:
Estimate Std. Error t value Pr(>|t|)
p1 1.881851   0.027430   68.61 2.27e-12 ***
p2 0.700230   0.009153   76.51 9.50e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.08202 on 8 degrees of freedom

Number of iterations to convergence: 7
Achieved convergence tolerance: 2.189e-06

Draw the fit on the plot by getting the prediction from the fit at 200 x-coordinates across the range of xdata

new = data.frame(xdata = seq(min(xdata),max(xdata),len=200))
lines(new\$xdata,predict(fit,newdata=new)) Getting the sum of squared residuals is easy enough:

sum(resid(fit)^2)

Which gives

 0.0538127

Finally, lets get the parameter confidence intervals.

confint(fit)

Which gives

Waiting for profiling to be done...
2.5%     97.5%
p1 1.8206081 1.9442365
p2 0.6794193 0.7209843

## Determing your machine name using R

July 11th, 2013

I was recently working with someone who was running thousands of R jobs on our Condor pool and some of them were failing.  As part of the diagnostic process, we needed to determine which machines in the pool were causing the problem.  My solution was to add the line

hostname

To the Bash script that eventually called R and the program he was running. This gives output that looks like this

badmachine.ourdomain.ac.uk

His solution was to do exactly the same thing in R. To do this, he added the following line to the beginning of his R script

(Sys.info()["nodename"])

Which produces output that looks like

nodename
"badmachine.ourdomain.ac.uk"

Either way, the job was done.

## Citing software in research papers

January 10th, 2013

R has a citation() command that recommends how to cite the use of R in your publications, information that is also included in R’s Frequently Asked Questions document.

To cite R in publications use:

R Core Team (2012). R: A language and environment for
statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
http://www.R-project.org/.

A BibTeX entry for LaTeX users is

@Manual{,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2012},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org/},
}

We have invested a lot of time and effort in creating R, please
cite it when using it for data analysis. See also
‘citation("pkgname")’ for citing R packages

This led me to wonder how often people cite the software they use.  For example, if you publish the results of a simulation written in MATLAB do you cite MATLAB in any way?  How about if you used Origin or Excel to produce a curve fit, would you cite that?  Would you cite your plotting software, numerical libraries or even compiler?

## xkcd style graphs in various languages

December 29th, 2012

xkcd is a popular webcomic that sometimes includes hand drawn graphs in a distinctive style.  Here’s a typical example In a recent Mathematica StackExchange question, someone asked how such graphs could be automatically produced in Mathematica and code was quickly whipped up by the community.  Since then, various individuals and communities have developed code to do the same thing in a range of languages.  Here’s the list of examples I’ve found so far

Any I’ve missed?

## Disagreement on operator precedence for 2^3^4

February 6th, 2012

My attention was recently drawn to a Google+ post by JerWei Zhang where he evaluates 2^3^4 in various packages and notes that they don’t always agree.  For example, in MATLAB 2010a we have 2^3^4 = 4096 which is equivalent to putting (2^3)^4 whereas Mathematica 8 gives 2^3^4 = 2417851639229258349412352 which is the same as putting 2^(3^4).  JerWei’s post gives many more examples including Excel, Python and Google and the result is always one of these two (although to varying degrees of precision).

What surprised me was the fact that they disagreed at all since I thought that the operator precendence rules were an agreed standard across all software packages.  In this case I’d always use brackets since _I_ am not sure what the correct interpretation of 2^3^4 should be but I would have taken it for granted that there is a standard somewhere and that all of the big hitters in the numerical world would adhere to it.

Looks like I was wrong!

## MATLAB and R ‘Rosetta Stone’

September 21st, 2009

I was recently given the task of converting a small piece of code written in R, the free open-source programming language heavily used by statisticians, into MATLAB which was an interesting exercise since I had never coded a single line of R in my life!  Fortunately for me, the code was rather simple and I didn’t have too much trouble with it but other people may not be so lucky since both MATLAB and R can be rather complicated to say the least.  Wouldn’t it be nice if there was a sort of Rosetta Stone that helped you to translate between the two systems?

Happily, it turns out that there is in the form of The MATLAB / R Reference by David Hiebeler which gives both the R and MATLAB commands for hundreds of common (and some not so common) operations.

While flicking through the 47 page document I noted that there are a few MATLAB commands for which David hasn’t found an R equivalent (possibly because there simply isn’t one of course).  For example, at number 161 of David’s document he describes the MATLAB command

yy=spline(x,y,xx)

which he describes as

‘Fit cubic spline with “not-a-knot” conditions (the first two piecewise cubics coincide,as do the last two), to points (xi , yi ) whose coordinates are in vectors x and y; evaluate at points whose x coordinates are in vector xx, storing corresponding y’s in yy.’

At the moment David doesn’t know of an R equivalent so if you are a R master then maybe you could help out with this extremely useful document?