Autarchy of the Private Cave

I’ve tried [briefly] Cantor (which also supports Octave and KAlgebra as backends), rkward, deducer/JGR, R Commander, and RStudio.

My personal choice was RStudio: it is good-looking, intuitive, easy-to-use, while powerful.

Next step would be using some R-equivalent of the excellent ipython’s Mathematica-like Notebook webinterface…

Original PDF.
My local copy.

Usually I’m using 10-fold (non-stratified) CV to measure the predictive power of the models: it gives consistent results, and is easy to perform (at least on smaller datasets).

Just came across the Akaike’s Infor­ma­tion Criterion (AIC) and Schwarz Bayesian Infor­ma­tion Criterion (BIC). Citing robjhyndman,

Asymp­tot­i­cally, min­i­miz­ing the AIC is equiv­a­lent to min­i­miz­ing the CV value. This is true for any model (Stone 1977), not just lin­ear mod­els. It is this prop­erty that makes the AIC so use­ful in model selec­tion when the pur­pose is prediction.
…
Because of the heav­ier penalty, the model cho­sen by BIC is either the same as that cho­sen by AIC, or one with fewer terms. Asymp­tot­i­cally, for lin­ear mod­els min­i­miz­ing BIC is equiv­a­lent to leave–v–out cross-​​validation when v = n[1-1/(log(n)-1)] (Shao 1997).

Want to try AIC and maybe BIC on my models. Conveniently, both functions exist in R.

1. Install the annotationTools R package:
   source(“http://bioconductor.org/biocLite.R”)
   biocLite(“annotationTools”)
2. Download full HomoloGene data file from ftp://ftp.ncbi.nlm.nih.gov/pub/HomoloGene/current
3. library(annotationTools)
4. homologene = read.delim(“homologene.data”, header=FALSE)
5. mygenes = read.table(“file with one entrez ID of the source organism per line.txt”)
6. getHOMOLOG(unlist(mygenes), taxonomy_ID_of_target_organism, homologene) [alternatively, wrap the call to getHOMOLOG into unlist to get a vector]

It might be easier to achieve the same results with a Perl script calling NCBI’s e-utils.

• R time series tutorial (2010, a website of the “Time Series Analysis and Its Applications: With R Examples” book)
• Statistics with R (2007)
• R for programmers PDF (2008, 104 pages, linked to from here)
• Brief R tutorial (2004)
• Statistical computing with R: a tutorial (2004)
• An introduction to R (from the official r-project website, should be always up-to-date)
• R tutorial (date unknown, definitely newer than 2005)

Sometimes there is a need to remove all the probesets, which have expression values below the minimal spike-in intensity on the Affymetrix microarray. The reasoning behind this procedure is simple: minimal-expression spike-ins represent the bottom margin of microarray sensitivity, and anything below that margin cannot be reliably quantified – which also means that both fold-change and p-value of expression variance will be unreliable for these probesets.

Here’s a simple R script to do just that. It is abundantly commented, and also contains an optional (commented out) fragment which allows the removal of more low-variance, low-intensity probesets.

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If you get this message when opening vignettes:

Error in openPDF(vif) :
getOption(‘pdfviewer’) is ”; please use ‘options(pdfviewer=…)’

and you are tired of running this command every time:

> options(pdfviewer=”okular”)

then you should check if your system-wide Renviron file has proper PDF viewer set:
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