search for: ahupxot02

Motivation: during each iteration, my code needs to collect tabular data (and use it only during that iteration), but the rows of data may vary. I thought I would speed it up by preinitializing the matrix that collects the data with zeros to what I know to be the maximum number of rows. I was surprised by what I found... # set up (not the puzzling part) x<-matrix(runif(20),nrow=4);

Let's assume that according to Anova(lm(y~a*b, data=d)) the a:b interaction is significant, and I would like to know if there are specific combinations of a and b levels that differ from the control group. Are there any caveats against simply looking at the p-values in the output generated by summary(lm(y~a*b,data=d))? The documentation for summary.lm says nothing about multiple comparison

Hello. To save the hassle of quitting and restarting R every time I rebuild the package I'm working on (for the purposes of this question called "roots") I would like to write a script cleanly remove the package from my R session and then load it again. Of course detach("package:roots") works for the native R objects in the package. However, the compiled C library remains

Hello. To save the hassle of quitting and restarting R every time I rebuild the package I'm working on (for the purposes of this question called "roots") I would like to write a script cleanly remove the package from my R session and then load it again. Of course detach("package:roots") works for the native R objects in the package. However, the compiled C library remains

Hello, and apologies for the upcoming naive questions. I am a biologist who is trying to teach himself the appropriate areas of math and stats. I welcome pointers to suggested background reading just as much as I do direct answers to my question. Let's say I have a function F() that takes variables (a,b,c,a1,b1,c1) and returns x, and I want to find the values of these variables that result in

Dear list: I am attempting to use what I thought would be a pretty straightforward practical application of Cox regression. I figure users of the survival package must have come across this problem before, so I would like to ask you how you dealt with it. I have set up an illustrative example and included it at the end of this post. I took a sample of 100 data points from each of two populations