search for: foxal

Hello all, A colleague at work set me the challenge to convert some MATLAB code into R, to see which is faster. We'd seen that benchmark comparing MATLAB 6.5 to R1.90 (and others), and so I thought that I should be able to get roughly comparable speeds. The code has lots of multiplications of matrixes, transposes, and MATLAB's "repmat". I did the code conversion, and R was about

Hi all, Easy question for you (which I failed to find the answer to in the FAQ etc). I've recently been forced to switch from linux to windows (currently windows NT), and my usual habit of putting lots of "cat" statements in slow functions to get an idea of the progress rate is no longer useful. Why -- because R waits until the function is completely finished before printing the cat

...it)) p96 <- predict.glm(my.glm,newdata=so293[,1:17],type="response") but I always get the fitted linear predictors from the original model, ie there doesn't seem to be acknowledgement of the new data. Linux on my machine, running R version 1.1.1 (will upgrade soon!). Cheers, Rob Foxall. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at st...

Full_Name: Rob Foxall Version: 2.2.0 OS: Windows XP Submission from: (NULL) (149.155.96.5) I was using multinom from nnet package, when I did something stupid -- I entered in an incorrect factor variable as response. This factor had only one level. Instead of R telling me not to be so dumb, it crashed, clicking on de...

Dear R-Help, I have written a function, which in simplified format can be represented as: temp.fn <- function(my.mean,my.sd){ Parameters <- list(mean = my.mean, sd = my.sd) curve(do.call(dnorm,c(list(x), Parameters)), from = my.mean-my.sd, to = my.mean+my.sd) } This works as I want it do. Wishing to immortalise this function into my very own package however, results in the following

Hello all, A really noddy question for you all: I''m trying without success to do some subhypothesis testing. Using simple anova model, with a toy dataset from a book. I have four factors A,B,C,D, and wish to test mu_C = mu_D. This is what I have tried: > contrasts(infants$group,how.many=1) <- c(0,0,1,-1) > contrasts(infants$group) [,1] A 0 B 0 C 1

Hello, I have an expression for the y axis, which I wish to rotate by 90 degrees (so that this label has the same orientation as for the x axis). Thought the graphical parameter crt (or srt) would do the trick, but haven't had any joy so far. Any ideas? (Using Version 1.2.2, linux, redhat 7.0) Cheers, Rob. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Dear R-helpers, I have a model created by lda, and I would like to use this model to make predictions for new or old data. The catch is, I want to do this without using the "predict" function, i.e. only using information directly from the foo.lda object to create my posterior probabilities. In anticipation of likely responses, I will be brushing up my lda knowledge using the

Dear R-help, I have a character vector, some elements will be numeric, some not, and some even empty. E.g.: temp1 <- c("abcd"," 2 ","") I'm only interested in the numeric elements, the rest I can just throw away. It is easy enough to loop through the vector: temp <- try(eval(parse(text=temp1[1])), silent=TRUE); class(temp) # try-error temp <-