search for: extendrange

I need to make a fairly complex animated graphic and decided to use grid for it. A very simple example of what I need: ##============================================================================== library(grid) grid.newpage() pushViewport(plotViewport()) pushViewport(viewport(xscale = extendrange(c(0, 100)), yscale = extendrange(c(0, 100)))) grid.xaxis() grid.yaxis() rectNames <- paste("r", 1:100, sep = "") for (i in 1:100) { grid.rect(x = unit(sample(0:100, 1), "native"), y = unit(sample(0:100, 1), "native"),...

Hi, I often need to extend the plot range to the right, but not to the left (for example: not below 0 so that log = "x" still works...). This could be a handy improvement of extendrange(): --- utils.R 2015-08-25 18:18:20.000000000 -0400 +++ utils.R 2018-04-25 17:21:25.000000000 -0400 @@ -30,6 +30,6 @@ ## Purpose: extend a range by a factor 'f' - on each side if(!missing(r) && length(r) != 2) stop("'r' must be a \"range\",...

Hello dear developers, I find myself often having the result of "range" oder "extendrange", which I want to create a sequence with. But "seq" needs two seperate arguments "from" and "two". Could an argument "range" be added? Otherwise I will have to create an object with the range (may come from a longer calculation), index twice from it and...

...BG_VALUE is inserted after it, but does not have the bundling flags set. This essentially means that if we start with a set of bundled instructions: MI1 [BundledSucc=true, BundledPred=false] MI2 [BundledSucc=false, BundledPred=true] Where MI1 is a spill, and MI2 is a different instruction, after ExtendRanges we end up with MI1 [BundledSucc=true, BundledPred=false] DBG_VALUE MI [BundledSucc=false, BundledPred=false] MI2 [BundledSucc=false, BundledPred=true] Since this happens after the final instruction scheduling, it results in broken schedules. I believe setting the bundling flags before invoking...

...BG_VALUE is inserted after it, but does not have the bundling flags set. This essentially means that if we start with a set of bundled instructions: MI1 [BundledSucc=true, BundledPred=false] MI2 [BundledSucc=false, BundledPred=true] Where MI1 is a spill, and MI2 is a different instruction, after ExtendRanges we end up with MI1 [BundledSucc=true, BundledPred=false] DBG_VALUE MI [BundledSucc=false, BundledPred=false] MI2 [BundledSucc=false, BundledPred=true] Since this happens after the final instruction scheduling, it results in broken schedules. I believe setting the bundling flags before invoking...

...rnorm(10) plot(y) I don't want to specify the x's manually, as this could become cumbersome when having multiple panels. I tried something like library(lattice) mp <- function(x, y, ...) { x <- 1:length(y) panel.xyplot(x, y, ...) } pp <- function(x, y, ...) { list(xlim = extendrange(1:length(y)), ylim = extendrange(y)) } set.seed(123) y <- rnorm(10) xyplot(y ~ 1, panel = mp, prepanel = pp, xlab="Index") but I was wondering whether there is a more straightforward way? By the way, if I do not specify the ylim in the prepanel function the plot is clipped, but read...

...the colors don't match. Attached is the R dataset of my data. Please help me tweak the map so the colors are properly assigned. # Get the entire US map for use later. us.map <- map("state", plot = FALSE, fill = TRUE) # Calculate the range of the map (with extra margins). xl <- extendrange(us.map$range[1:2]) yl <- extendrange(us.map$range[3:4]) library(maps) library(lattice) library(latticeExtra) library(RColorBrewer) # creates nice color schemes library(classInt) plotclr <- brewer.pal(nclr,"PuRd") class <- classIntervals(NatSTSummaryHigh.abi$STMean, nclr, style...

...on is to replace this ... if (is.null(xlim)) delayedAssign("lims", { pu <- par("usr")[1:2] ll <- if (par("xlog")) 10^pu else pu if (par("xaxs") == "r") extendrange(ll, f = -1/27) else ll }) with this ... if (is.null(xlim)) delayedAssign("lims", { pu <- par("usr")[1:2] if (par("xaxs") == "r") pu <- extendrange(pu, f = -1/27) ll...

Dear R-experts, I have two problems: PROBLEM (1) ----------- I want to produce a very small png file (35 x 18 px) that contains a histogram without a figure region or margins, only the pure heights. In the base graphic system this is simple: png(filename = "hist.png", res = 72, width=35, height=18) par(mar=c(0,0,0,0), oma=c(0,0,0,0)) hist(rnorm(100),

Hi R-Users, I have a time series of bivariate observations (x,y,t). I've developed a few panel routines to explore certain aspects of the data, and now would like to break the time series up into smaller chunks to explore the temporal trends. I would like to know if anyone has any experience breaking up time series with lattice. Base graphics offers coplot, utilizing the

...lass' without previous declaration /usr/lib/gcc/x86_64-redhat-linux/4.7.0/plugin/include/tm-preds.h:191:68: error: invalid type in declaration before ';' token /builddir/build/BUILD/dragonegg-3.0.src/src/Constants.cpp: In member function '{anonymous}::BitSlice {anonymous}::BitSlice::ExtendRange(SignedRange, llvm::TargetFolder&) const': /builddir/build/BUILD/dragonegg-3.0.src/src/Constants.cpp:188:7: error: 'BYTES_BIG_ENDIAN' was not declared in this scope /builddir/build/BUILD/dragonegg-3.0.src/src/Constants.cpp: In member function '{anonymous}::BitSlice {anonymous}::B...

..."==" fm1 all.equal(predict(fm1, xx), predict(fm3, xx)) ## BTW: The chosen knots and coefficients are mat <- with(fm1, cbind(dirs, cuts=c(cuts), coef = c(coefficients))) ## Plots : fine grid for visualization: instead of xx <- seq(x[1], x[length(x)], length.out = 1024) rnx <- extendrange(x) ## to extrapolate a bit xx <- do.call(seq.int, c(rnx, list(length.out = 1200))) cbind(f = f(xx), sspl = predict(m.sspl, xx)$y, mars = predict(fm1, xx)) -> fits plot(x,y, xlim=rnx, cex = 1/4, col = adjustcolor(1, 1/2)) cols <- c(adjustcolor(2, 1/3), adjustcolor(4,...

...predict(fm1, xx), predict(fm3, xx)) > > ## BTW: The chosen knots and coefficients are > mat <- with(fm1, cbind(dirs, cuts=c(cuts), coef = c(coefficients))) > > ## Plots : fine grid for visualization: instead of xx <- seq(x[1], x[length(x)], length.out = 1024) > rnx <- extendrange(x) ## to extrapolate a bit > xx <- do.call(seq.int, c(rnx, list(length.out = 1200))) > > cbind(f = f(xx), > sspl = predict(m.sspl, xx)$y, > mars = predict(fm1, xx)) -> fits > > plot(x,y, xlim=rnx, cex = 1/4, col = adjustcolor(1, 1/2)) > cols <- c(adj...

...us namespace)::LiveDebugValues::OpenRangesSet&, llvm::UniqueVector<(anonymous namespace)::LiveDebugValues::VarLoc>&) [clone .isra.207] (in /nix/store/06dpmfn74zrmman8nq8p96dg50lwbxhg-llvm-3.9.0-lib/lib/ libLLVM-3.9.so) ==27195== by 0x56A3EE5: (anonymous namespace)::LiveDebugValues::ExtendRanges(llvm::MachineFunction&) [clone .constprop.259] (in /nix/store/06dpmfn74zrmman8nq8p96dg50lwbxhg-llvm-3.9.0-lib/lib/ libLLVM-3.9.so) ==27195== by 0x57214A0: llvm::MachineFunctionPass::runOnFunction(llvm::Function&) (in /nix/store/06dpmfn74zrmman8nq8p96dg50lwbxhg-llvm-3.9.0-lib/lib/ libLLV...

Dear all, When I use parallel function in lattice package, I want to label the y-axis with customized numbers. Like this: parallel(~iris[1:4] | Species, iris,horiz=FALSE,common.scale=TRUE, scales=list(y=list(at=c(0,2,3)))) But only "Min" label in the y-axis, nothing happened. Could anyone help me? Thanks. Regards, Pengcheng Yang

Hi all, I'm working on some plots using lattice (R 2.10.1), and have entered the polish phase. I've produced a satisfactory pair of xyplots ( http://imgur.com/EyXGi.png), but would like to align the y-axes of the top and bottom plots. I assume that I need to adjust axis padding or something, but I can't figure this one out. Thanks for any help! Dave -- Post-doctoral Fellow

?axTicks says: usr: numeric vector of length four, defaulting to ?par("usr")? giving horizontal (?x?) and vertical (?y?) user coordinate limits. but this is not how the function is implemented -- in fact 'usr' should be a vector of length two corresponding to the appropriate elements of par("usr") [1:2 if side is 1 or 3, 3:4 if side is 2 or

Hi everybody, I want an x-axis which has xlim=c(max, min) rather than xlim=c(min, max) in order to reflect the type of the process (cooling): library(lattice) myprepanel <- function(x,y,...) list(xlim=rev(range(x))) x <- rep(1:10, 100) z <- factor(sample(10, 1000, T)) y <- rnorm(1000, x, as.numeric(z)) xyplot(y~x|z, scales=list(x="free"), prepanel=myprepanel) This works

...st be a function or an expression containing 'x'") expr <- sexpr if (is.null(ylab)) ylab <- deparse(sexpr) } if (is.null(xlim)) delayedAssign("lims", { pu <- par("usr")[1L:2] if (par("xaxs") == "r") pu <- extendrange(pu, f = -1/27) if (par("xlog")) 10^pu else pu }) else lims <- xlim if (is.null(from)) from <- lims[1L] if (is.null(to)) to <- lims[2L] lg <- if (length(log)) log else paste(if (add && par("xlog")) "x",...

How can I do automatic knot selection while fitting piecewise linear splines to two variables x and y? Which package to use to do it simply? I also want to visualize the splines (and the scatter plot) with a graph. Anupam [[alternative HTML version deleted]]