Extract objects from a list

When using Rmpi to send processes to many nodes, it is convenient to create a list of tasks that are assigned to nodes as they become available. In my case, I was working through a large factorial set of simulations and needed to use a unique set of variable values for each task. Rather than assign these variables individually within the slave function, I decided to extract the variables directly from the list using a variation of the following code:

mylist=(list(a=1,b=2,c="string1",d=list("r"=2,"z"="string2")))

for(i in 1:length(mylist)){
  ##first extract the object value
  tempobj=mylist[[i]]
  ##now create a new variable with the original name of the list item
  eval(parse(text=paste(names(mylist)[[i]],"= tempobj")))
}

> print(a)
[1] 1
> print(b)
[1] 2
> print(c)
[1] "string1"
> print(d)
$r
[1] 2

$z
[1] "string2"

If there is a more elegant way to do this, please let me know in the comments.

Parallel computing in R

Lately I have been running R in parallel on a Beowulf cluster using the Rmpi package. Instrumental in my ability to do this were two excellent tutorials:

The Division of Biostatistics at UCSF provides some of the background required to get things running on the Linux side of things. (Note: sometimes the url for this site comes up with an extra "biostat" in the address -- just delete that and the page should load.

The Acadia Centre for Mathematical Modelling and Computation has a variety of sample scripts to point you in the right direction. Once you read through the code, it becomes very easy to set up a template you can re-use in the future.

Execute system commands within an R Script

To execute a system command from within an R script, just use system(). This is handy for zipping large output files on the fly.

write.csv(mydat, "mydat.csv")
system("gzip mydat.csv", wait=FALSE)

Offset in glm ()

To add an offset to the linear predictor of a generalized linear model (or models from the survival package such as coxph and clogit), use offset(x) in the formula. This will add an offset to the linear predictor with known coefficient 1.

Including arguments in R CMD BATCH mode

When you have multiple computers or processors at your disposal and wish to run the same script with different arguments, use the following at the command line (here described for Linux; remove the linebreak, it is just there for display purposes):

$ R CMD BATCH --no-save --no-restore '--args a=1 b=c(2,5,6)'
test.R test.out &

Where test.R is the R script file you wish to run and test.out is a text file to include the screen output of the R terminal. A key point here is that each argument must have no spaces because --args is space delimited.

To include the variables listed in --args, adapt the following code from test.R:

##First read in the arguments listed at the command line
args=(commandArgs(TRUE))

##args is now a list of character vectors
## First check to see if arguments are passed.
## Then cycle through each element of the list and evaluate the expressions.
if(length(args)==0){
    print("No arguments supplied.")
    ##supply default values
    a = 1
    b = c(1,1,1)
}else{
    for(i in 1:length(args)){
         eval(parse(text=args[[i]]))
    }
}
print(a*2)
print(b*3)

This produces the following in test.out:

> print(a*2)
[1] 2
> print(b*3)
[1]  6 15 18

Variance-Covariance Matrix in glm

This is a small function Venables and Ripley provide in their MASS book. You don't need it anymore because vcov() has a method for the glm class. However, it is useful to see how to extract bits from a fitted model object.

vcov.glm<-function(obj){
  #return the variance-covariance matrix of a glm object
  #from p. 188 in Venables and Ripley. 2002. 
  #Modern Applied Statistics With S. Springer. New York.
  so <- summary(obj,corr=F)
  so$dispersion * so$cov.unscaled
}

Visualizing Data

Sometimes it is helpful to see the plots created from the example
files within R libraries. Of course you can paste them directly
into R, but this web page allows you to view the images within the
context of the help files:

R Graphical Manuals

Also, check out this link for the R Graph Gallery.