similar to: R-beta: default paper size

Questions on image: 1) How can I put labels on the x and y axes? 2) How to tell it to use e.g. 32 grey levels (not some colour map)? 3) How to know what the legend is (i.e. each grey level = what z value)? Thanks very much for any help. BTW I was wondering if persp was on the To Do list. That would be great! Bill Simpson

I am a long-time Mac user. R is way behind on that platform, which is one reason why I decided to try out Linux on intel. I have the system set up and have installed R (using Slackware, which is only distribution my tech services wants me to use). It went very smoothly, thanks everyone behind R! I installed R in the directory /usr/local/bin/R-0.50-a4/ Not sure if this is a sensible set-up.

I have a question on the use of return(). (Nothing on it in the docs I have) The test code below gives the error: Error: Object "x" not found when I do: thingy2(). How should it be fixed? Thanks very much for any help! (My original solution to this sort of problem was to use global variables x<<-... y<<-...) Bill Simpson ----------------------------- thingy<-function(k)

Dear R Users: Here is a nice simple question: On Version 1.0.0, where is the file that has the paper size for plots? UNIX Dec Alpha please? (Really dumb. I know.) Sincerely, Erin -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

I do: x<-rnorm(1000) quantile(x,c(.025,.975)) 2% 98% -1.844753 1.931762 Since I want to find a 95% confidence interval, I take the .025 and .975 quantiles. HOWEVER R says I have the 2% (not 2.5%) and 98% (not 97.5%) points. Is it just rounding the printed 2% and 98%, or is it REALLY finding .02 and .98 points instead of .025 and .975? Thanks for any help. Bill Simpson

I do: x<-rnorm(1000) quantile(x,c(.025,.975)) 2% 98% -1.844753 1.931762 Since I want to find a 95% confidence interval, I take the .025 and .975 quantiles. HOWEVER R says I have the 2% (not 2.5%) and 98% (not 97.5%) points. Is it just rounding the printed 2% and 98%, or is it REALLY finding .02 and .98 points instead of .025 and .975? Thanks for any help. Bill Simpson

When I look at ppoints I see: ppoints<-function (x) { n <- length(x) if (n == 1) n <- x (1:n - 0.5)/n } However Venables & Ripley (2nd ed, p 165) say ppoints() should return (i-1/2)/n for n>=11; (i-3/8)/(n+1/4) for n<=10. The version below should work as described: ppoints<-function (x) { n <- length(x) if (n <= 10) (1:n - 0.375)/(n + 0.25) else (1:n - 0.5)/n

I want to do something like this in R. If I have three vectors > a1 [1] 1 2 3 > a2 [1] 4 5 6 > a3 [1] 9 10 7 I want to compute 1. A vector that is the mean at each serial position of a1, a2, and a3. so in this example it would have the contents 4.667, 5.667, 5.333333 2. A vector that is the SD at each serial position of a1, a2, and a3. so in this example it would have the contents

version .62: --------------------------------------------- > ?qpois The Poisson Distribution dpois(x, lambda) ppois(q, lambda) qpois(p, lambda) rpois(n, lambda) Arguments: x: vector of (positive) quantiles. p: vector of probabilities. n: number of random values to return. lambda: vector of positive means.

I am running R-0.61.1 on linux under X. If I kill the R Graphics window (click on X box in upper right corner), then subsequently do x11(), R crashes. I find that things only work the right way if I close the graphics window using dev.off(). I was wondering if maybe it could be arranged that closing the graphics window via click would seem to R to be equivalent to typing dev.off(). BTW thanks

Can someone please tell me to make a density histogram? hist makes one with count or relative frequency on the y-axis. I want the density, which is (rel freq)/(bin width) In the help I see: intensities: values f^(x[i]), as estimated density values. If `all(diff(breaks) == 1)', they are the relative frequencies `counts/n' and in general satisfy

This surprised me. Is it the way it is supposed to be? > x<-c(1,2,3,4,5) > y<-c(1.2,1.3,1.4,1.5,NA) > x-y [1] -0.2 0.7 1.6 2.5 NA > (x-y)^2 [1] 0.04 0.49 2.56 6.25 NaN >>>so NA^2 = NaN? Why not still NA? > sum((x-y)^2) [1] NaN >>>yes that is reasonable So if you ever have a data set with missing observations (NAs), you can't do any nlm() least

If I do fit<-lm(y~x) Is it possible to access the SE of the slope? (Analogous to getting the slope like this: fit$coef[2]) If not, it would be handy. (I want SE of 1/slope, and an approx way is fit$se[2]/(fit$coef[2]^2)) Thanks for any help. Bill Simpson -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I tried to ?ms, ?nls and apparently these aren't implemented on R yet. However I seem to remember postings on this list having to do with fitting nonlinear models (no I don't mean GLM type fits, I have a REAL nonlinear model: y=ax^b + c). So please tell me if it is possible to fit nonlinear models in R (by least squares or ML). Thanks! Bill Simpson

I include the argument pty="s" to image, but still when I make an image by the method > postscript("rstuff/test.ps") > tauseq<-seq(0,1,.5) > cif2d.image(x,,y,tauseq) > dev.off() the image plot created is NOT square. I thought pty="s" would make it square. Generally it would be nice if the image saved to disk were like the one we see while in R

Thanks very much Douglas for the pointer to nlm. Maybe the "Notes on R" maintainer can add at least a mention of nlm in the section on nonlinear fitting? I never did nonlinear fitting in S-Plus before, so I have nothing to unlearn, but I was hoping someone could show me how to do a least squares fit with nlm. example: x<-c(1,2,3,4,5,6) y<-.3*x^-.6 +.2 y<-y+rnorm(6,0,.01)

Thanks very much Douglas for the pointer to nlm. Maybe the "Notes on R" maintainer can add at least a mention of nlm in the section on nonlinear fitting? I never did nonlinear fitting in S-Plus before, so I have nothing to unlearn, but I was hoping someone could show me how to do a least squares fit with nlm. example: x<-c(1,2,3,4,5,6) y<-.3*x^-.6 +.2 y<-y+rnorm(6,0,.01)

I am using R Version 0.62.0 Unstable-snapshot (April 26, 1998) on Linux. I use the AfterStep window manager. Problem. Once upon a time, a workspace was saved. But at the end of this session, on quitting R I say "no", don't save workspace. On starting up, there are all the old objects again! I take it that the workspace is saved as ~/.RData. So is the only solution to manually rm

I know how to first set postscript output, then execute plot or image command to create a postscript file of a plot or image. I was wondering if it is possible to first create the plot or image on screen so it can be viewed, THEN save it to file? Without doing plot() or image() again? (I ask because I have a complicated image computation that takes about 1-2 hrs to complete. I would like to

Here is a programming question. The code I am using is quite slow and I was wondering if there is a way to get rid of the for loop. I am dealing with "interaction" in 2x2 table, and am using Edwards's G_I (Likelihood, p. 194). I label the cells in the table as follows stim response "y" "n" total -------------------------------- y hit miss nsignal