An interesting blog post at http://blogs.sas.com/jmp/index.php?/archives/298-JMP-Into-R!.html on using the new JMP 9 with R, and quite possibly using SAS as well.
Example Code-
Here’s the R integration JSL code used to run the bootstrap
rconn = R Connect();
rconn << Submit(“\[
library(boot)# Load Boot package
library(boot)RStatFctn <- function(x,d) {return(mean(x[d]))}
b.basic = matrix(data=NA, nrow=1000, ncol=2)
b.normal = matrix(data=NA, nrow=1000, ncol=2)
b.percent =matrix(data=NA, nrow=1000, ncol=2)
b.bca =matrix(data=NA, nrow=1000, ncol=2)for(i in 1:1000){
rnormdat = rnorm(30,0,1)
b <- boot(rnormdat, RStatFctn, R = 1000)
b.ci=boot.ci(b, conf =095,type=c(“basic”,”norm”,”perc”,”bca”)) b.basic[i,] = b.ci$basic[,4:5]
b.normal[i,] = b.ci$normal[,2:3]
b.percent[i,] = b.ci$percent[,4:5]
b.bca[i,] = b.ci$bca[,4:5]
}
]\”));
b_basic= rconn << Get(b.basic);
b_normal = rconn << Get(b.normal);
b_percent= rconn << Get(b.percent);
b_bca = rconn << Get(b.bca);
rconn << Disconnect();
Using the R Connect() JSL command and assigning it to the object “rconn”, the code sends messages to the JSL scriptable object “rconn” to submit R code via the Submit() command and to retrieve R matrices containing the bootstrap confidence intervals back via the Get() commands.
and I also found interesting what the write has to say about using JMP (for visual analysis) and SAS (bigger datasets handling) and R (for advanced statistics) together
Other standard JMP tools such as the Data Filter can help to explore these results in ways that cannot easily and quickly be done in R
and
With a little JSL and the statistical and graphics platforms of JMP coupled with the breadth and variety of packages and functions in R, one can build complete easy-to-use applications for statistical analysis.
JMP can also integrate with SAS, which adds the ability to work with large-scale data through the file-based system as well as the depth and advanced capabilities of SAS procedures. With these seamless integrations, JMP can become a hub that enables you to connect with both SAS and R, as well as provide unique statistical features such as the JMP Profiler and interactive graphic features such as Graph Builder
and in the meanwhile here is a data visualization of a frequency analysis of various words bundled together from xkcd.com
DECISION STATS 