Hello, Bugs world!

Here is my first Bayesian code modified from the sample code for the Chapter 3 of Bayesian Adaptive Methods for Clinical Trials. The R code depends on BRugs that interfaces with Openbugs (manual is here).

IBS alternative

The publicly available IBS (Identity by State) macro in SAS is quite slow, and has a bug that IBS is not adjusted for call rate. I made an alternative implementation which is about 1/2 or 1/3 faster. However, SAS apparently handles the standard correlation calculations much much faster, and resulting Pearson correlation, Kendall correlation and Spearman correlation may give similar measurements to IBS (code is here, data include about 1000 markers clustered in candidate genes in 400 subjects, primary Caucasians). Therefor I guess in certain Kernel application, we can use usual correlation to replace IBS.

64bit perl debugger

It turns out Active Perl does not support 64bit debugger in 64 bit windows under PDK. From here:

The PDK's debugger was written for 32-bit Windows only. It's not available on ANY other platform, hence the 64-bit Windows PDK does not include a debugger. This has been documented in the release notes since the first 64-bit Windows PDK appeared (PDK 8.0).

There are no plans to update the PDK debugger, since a viable (and better) alternative exists in Komodo IDE. If you want a debugger for 64-bit, the multi-platform, multi-language Komodo IDE debugger is our suggested choice.

But still we can install 32 bit Active Perl for windows and use an older version of PDK (version 6 works).

sample code - MMRM

the following code implements the analyses suggested at the lower right corner of page 10 (i.e., page 312) of this paper for genetic predictors.

pairs() and more

Getting genetics done has a piece of code to make a more informative pairwise scatter plot than pairs()

# panel.smooth function is built in.
# panel.cor puts correlation in upper panels, size proportional to correlation
panel.cor <- function(x, y, digits=2, prefix="", cex.cor, ...)
{
    usr <- par("usr"); on.exit(par(usr))
    par(usr = c(0, 1, 0, 1))
    r <- abs(cor(x, y))
    txt <- format(c(r, 0.123456789), digits=digits)[1]
    txt <- paste(prefix, txt, sep="")
    if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
    text(0.5, 0.5, txt, cex = cex.cor * r)
}
 
# Plot #2: same as above, but add loess smoother in lower and correlation in upper
pairs(~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width, 

data=iris,lower.panel=panel.smooth, upper.panel=panel.cor, 
      pch=20, main="Iris Scatterplot Matrix")

From the comments, we see alternatives are pairs.panels() from package psych and chart.Correlation() from package PerformanceAnalytics.