similar to: png(): Linux vs Windows

Hi Folks, There has been past correspondence regarding Catherine Loader's Bell Labs (oops, Lucent) paper "Fast and Accurate Computation of Binomial Probabilities" which gives the algorithm on which R's dbinom() is based. The original URL given in the R documentation "?dbinom" is: http://cm.bell-labs.com/cm/ms/departments/sia/catherine/dbinom but this link is

Hi Folks, I'm wondering if there's a compact way to achieve the following. The "dream" is that, by analogy with rep(c(0,1),times=c(3,4)) # [1] 0 0 0 1 1 1 1 one could write rep(c(0,1),times=c(3,4,5,6)) which would produce # [1] 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 in effect "recycling" x through 'times'. The objective is to produce a vector of

Hi Folks, Maybe I've missed it already being available somehow, but if the following isn't available I'd like to suggest it. If you're happy to let plot() choose its own limits, then of course plot(x,y) will do it. If you know what limits you want, then plot(x,y,xlim=c(x0,x1),ylim(y0,y1) will do it. But sometimes one would like to a) make sure that (e.g.) the y-axis has a

Hi again! Following up my previous posting below (to which no response as yet), I have located a report which situates this type of question in a longitudinal modelling context. http://www4.stat.ncsu.edu/~dzhang2/paper/glm.ps Generalized Linear Models with Longitudinal Covariates Daowen Zhang & Xihong Lin (This work seems to originally date from around 1999). They consider an outcome Y,

I am interested in studying the binary representation of numerics (doubles) in R, so am looking for possibilities of output of the internal binary representations. sprintf() with format "a" or "A" is halfway there: sprintf("%A",pi) # [1] "0X1.921FB54442D18P+1" but it is in hex. The following illustrate the sort of thing I want: 1.1001 0010 0001 1111

Hi Folks, I'm a bit puzzled by the following (example): N<-factor(sample(c(1,2,3),1000,replace=TRUE)) unique(N) # [1] 3 2 1 # Levels: 1 2 3 So far so good. Now: contrasts(N)<-contr.treatment(3, base=1, contrasts=FALSE) contrasts(N) # 1 2 # 1 1 0 # 2 0 1 # 3 0 0 whereas: contr.treatment(3, base=1, contrasts=FALSE) # 1 2 3 # 1 1 0 0 # 2 0 1 0 # 3 0 0 1 contr.treatment(3, base=1,

Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match

Hi Folks, Compare print(1234567890,digits=4) # [1] 1.235e+09 print(1234567890,digits=5) # [1] 1234567890 Granted that digits: a non-null value for 'digits' specifies the minimum number of significant digits to be printed in values. how does R decide to switch from the "1.235e+09" (rounded to 4 digits, i.e. the minumum, in "e" notation) to

Greetings all! I have been searching the Site for a function, say "subsets", such that for instance subsets(10) would return a (say) matrix of indices to the 2^10 subsets of N items -- perhaps in the form of 2^10 rows each of which is 10 entries each either TRUE or FALSE. Or 1 or 0. Or ... I can of course write my own, using good old looping technology or similar, but it would be

Hi Folks, I'm running Debian-4.0 Etch, installed last September from a DVD, and regularly updated as things arise. I have R version 2.4.0 Patched (2006-11-25 r39997) installed (initially at the time of first installation of Debian, as provided by Debian), along with a variety of packages. I'd like to be able to connect to the CRAN repositories for Debian R, for updates etc. When I visit

Greetings All, Out of curiosity, I've just done a very primitive experiment: Obj <- list(Fun=sum, Dat=c(1,2,3,4)) Obj$Fun(Obj$Dat) # [1] 10 That sort of thing (much more sophisticated) must be documented mind-blowingly somewhere. Where? Where I stand right now: The above (and its immediately obvious generalisations, like Obj$Fun<-cos) is all I know about it so far. Ted.

Greetings! I would like to be able to specify a fixed (say) lower limit for plotting, while leaving the upper limit "floating, when plotting. The context is that the maximum in the data to be plotted is unpredictable, being the consequence of a simulation, whereas I know that it cannot be less than (say) 0; and I want to fix the lower limit at 0 in any plot, leaving the upper limit to be

Hi Folks, Say I have 2 continuous variables X,Y. I can of course plot (X,Y) with plot(X,Y,pch="+",col="blue") say, and add the regression line from lm(Y~X) by extracting the coefficients 'a' of Intercept and 'b' of X from Y.lm <- lm(Y~X). Now, however, I want to have not only a general explanatory title such as main="Plot of Y against X"

Thanks much Ted. I actually had just tried what you suggest here before you posted, and resolved the problem. Thanks also for the other tips. I wrote x = as.vector(c(1:12)) because I thought that the mode of x might be the problem, the error message pointing to .Random.seed notwithstanding. On a related note, I did a brief test a couple weeks back where I ran a million random samples of 3 from

Hi Folks, I'm advising someone who's a beginner with R, and therefore wants the simplest answer possible. The issue is to produce a plot using plot(x,y,...) where, on the X-axis, the tick-marks should be on the lines of: -- Range of X-axis: 0:1000 -- tick-marks labelled "0","200",...,"800","1000" -- unlabelled tick-marks every 50 from 0 to

Hi Folks, This must be easy but I've not managed to locate the solution! Basically: I'm using sink() to save successively obtained results, e.g. I construct a set of regression coefficients etc. with names rows and columne (I want to see the names in the output) as an object (say "Object"), and then I emit it with print(Object) So far so good. But I also want to print a

Hi Folks, I have been using contour() to produce some contour plots (of a spatially-smooted density produced by kde2d()), with very satisfactory results. I now want access to the coordinates of the points on the contours, and it would seem that contour() does not return a value, so there is nothing from which these could be extracted. However, apparently contourLines() does, and it seems to be

Greetings Moderators! I moderated the message below just now (one of two identical test messages). I rejected it, with a covering note to the author (John Munroe <munddr at gmail.com>, who does not appear to be subscribed to the list) that I was doing so because approving it would serve no useful purpose, and pointing out that it had been held because the message headers matched a filter

Greetings, All! I want to draw contour lines in red, using contour(), but also have the contour labels (for the level-values) in black so that they will stand out against a coloured background already generated using filled.contour() (the background shades from green at low levels of "risk" to red at high levels). In any case, contour labels in red are already somewhat inconspicuous

Hi Folks, I'd like to know how hist() decides how many cells to use when it ignores my "suggestion" to use say 'hist(...,breaks=50)'. More specifically, I have the results of 10000 simulations, each returning an 8-vector, therefore 8 variables each with 10000 values. Some of these 8 have somewhat skew distributions. Say one of these 8 variables is X. I ask for H <-