similar to: xyplot: making strip function aware of panel.number

Full_Name: Ben Tyner Version: 2.2.0 OS: i686-pc-linux-gnu Submission from: (NULL) (128.210.141.240) My appologies if this has already been fixed, but I didn't see it in the tracking system yet so I thought I'd report it. Demonstration: xyplot(Petal.Length ~ Petal.Width | Species, iris, strip = strip.custom(style = 1, var.name = expression(beta),

I like the functionality provided by outer=TRUE, but when it comes time to place separate xlabs or ylabs, I always end up 'eyeballing' it on a case-by-case basis. For example, ##begin example require(lattice) cars.lo <- loess(dist ~ speed, cars) print(xyplot(cars.lo$residuals+cars.lo$fitted~cars.lo$x, strip=FALSE, outer=TRUE, layout=c(1,2),

I would like a tick mark for each month; for example, xyplot(runif(365)~I(as.Date("1999-01-01") + 1:365), scales=list(x=list(format="%b %Y",tick.number=12))) I know I could make x numeric and use 'at' and 'labels', but I was wondering if there is a more direct route I'm missing. (In particular, one that doesn't have to be modified for new data).

When plotting over multiple pages in lattice, I'd like to be able to have "same" scales within a page, but "free" scales between pages. In other words, something like: z<-data.frame(x=1:100, y=runif(100), d=rep(1:2,50), p=rep(1:2,each=50)) plot<-xyplot(y~x|d*p, data=z,

Say I have library(lattice) x<-runif(256) y<-runif(256) f<-gl(16,16) g1<-rep(1:4,each=64) g2<-rep(1:4,times=64) plot<-xyplot(y~x|f, groups=g1, pch=as.character(1:4), panel=function(x,y,subscripts,groups,...){ panel.superpose(x,y,subscripts,groups,...) }) print(plot) Currently, both

The following tinkers with the strip labels, where the different panels are for different levelf of a conditioning factor. tau <- (0:5)/2.5; m <- length(tau); n <- 200; SD <- 2 x0 <- rnorm(n, mean=12.5, sd=SD) matdf <- data.frame( x = as.vector(sapply((0:5)/2.5, function(s)x0+rnorm(n, sd=2*s))), y <- rep(15+2.5*x0, m), taugp = factor(rep(tau, rep(n,m)))) names(matdf)

I wish to create a multipanel plot (map) from several datasets ("d" and "q" in the example below). I can condition the main xyplot statement on the "site" variable, but I don't know how to pass a conditioning variable to panel.xyplot plot so that the x-y coordinates from dataset q are only plotted at the appropriate site. library(lattice) d <-

I didn't do the compile; is there a way to check whether that was used? If not, I'll inquire with our sysadmin and report back. In any case, my suggestion was motivated by the fact that some parts of R use OpenMP while others do not, in the hope that the former could have their OpenBLAS omelet without breaking the OpenMP eggs, so to speak. On 01/09/2018 06:41 PM, Keith O'Hara

Hi Does rJava offer a way to instruct the JVM to perform a garbage collection? Regards Ben

I was hoping to fit along the lines of g<-gl(20,5) y<-runif(100) fit<-lme(fixed=y~g,correlation=corAR1(0,~1|g)) But I get the error "Incompatible formulas for groups in "random" and "correlation"" Any help would be greatly appreciated. Ben

A kind member of R-core suggested this is due to a misconfiguration on my system, and to post it to the mailing list for troubleshooting. When trying to build R version 4.3.3, in at least two places during the process it gives LaTeX errors of the form: (example 1) you should 'make docs' now ... make[1]: Entering directory '/home/btyner/R-4.3.3/doc' creating

Andreas, How right you are! Still, I find it curious that in the context of the while(TRUE) loop, I am allowed to do this 653 times, with failure on the 654th attempt. Perhaps there is something asynchronous going on? If I eliminate the looping, it does indeed fail (as expected) on the first attempt to close the pipe. Regards Ben On 12/6/19 2:04 AM, Andreas Kersting wrote: > Hi

Not sure if this is a bug, so posting here first. If I run: ?? cnt <- 0L ?? while (TRUE) { ? ? ?? cnt <- cnt + 1L ? ? ?? p <- pipe("echo /dev/stdin > /dev/null", open = "w") ? ? ?? writeLines("foobar", p) ? ? ?? tryCatch(close(p), error = function(e) { print(cnt); stop(e)}) ?? } then once cnt gets to around 650, it fails with: ?? [1] 654 ??

Background: I'm a student of Prof. Cleveland at Purdue University. Eventually, we'd like to release a new version of the loess routine in R. For starters, this implementation would have support for local polynomial degree 3, better control over the number of cells in the KD tree, and perhaps a better solution in higher predictor dimension. I see that Prof. Ripley was responsible for

So imagine rpois is changed, such that the storage mode of its return value is sometimes integer and sometimes numeric. Then imagine the case where lambda is itself a realization of a random variable. Do we really want the storage mode to inherit that randomness? On 1/19/20 10:47 AM, Avraham Adler wrote: > Maybe there should be code for 64 bit R to use long long or the like? > > On

Dear R-users I have 5 dependent variables (y1 to y5) and one independent variable (x) and 3 conditioning variables (m, n, and 0). Each of the conditioning variables has 2 levels. I created 2*4 panel plots. xyplot(y1+y2+y3+y4+y5 ~ x | m*n*o,layout = c(4,2)) I would like to reorder the 8 panels. I tried to use index.cond (e.g., index.cond = list(c(1,3,2,4,5,7,6,8)) but it didn't work out.

Full_Name: Benjamin Tyner Version: 2.6.0 RC 2007-10-01 r43043 OS: WinXP Submission from: (NULL) (171.161.224.10) platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status RC major

Hello, I am using the xyplot() function to create a series of scatterplot panels with lines of best fit. To draw the lines of best fit for each panel, I am using a panel function. Here's an example: > species <- as.character(c(rep(list("A", "B", "A"), 10), "B")) > year <- as.character(c(rep(list("2009", "2009",

In qqmath, how would one go about having 'distribution' change with panel.number? I've tried set.seed(1) mydata <- data.frame(ind = factor(rep(2:4, each = 100))) mydata$val <- rt(300, df=rep(2:4, each = 100)) plot<-qqmath(~ val | ind, layout=c(3,1), data = mydata, prepanel = function(x, distribution, ...) {

Dear R users, this is a follow up of this message http://tolstoy.newcastle.edu.au/R/e6/help/09/05/13897.html I'm reproducing the core of it for convenience. > // > / data(Oats, package = "MEMSS") / > / tp1.oats <- xyplot(yield ~ nitro | Variety + Block, / > / data = Oats, / > / panel = function(x, y, subscripts, ...) { /