similar to: Plot 2 time series with different y axes (left and right)

Hi It is usually not recommended but if you insist maybe library(plotrix) ?twoord.plot twoord.plot(lx=D[,1],ly=D[,2], rx=D[,1], ry=D[,3]) or plot.yy(x=D[,1],yright=D[,3], yleft=D[,2]) which allows only one x axis (see below). Cheers Petr plot.yy <- function (x, yright, yleft, yleftlim = NULL, yrightlim = NULL, xlab = NULL, yylab = list(NA, NA), pch = c(1, 2), col = c(1,2), linky

Thank you very much peter. It worked out nicely. I have additional question. How can I get Y-axis on log-scale? Thank you very much in Advance, Eliza UoS PP ________________________________ From: PIKAL Petr <petr.pikal at precheza.cz> Sent: 23 November 2017 16:22:39 To: Eliza Botto; r-help at r-project.org Subject: RE: adding percentage secondary y-axis Hi It is usually not

Dear useRs, I have this dataset (D) with three columns. > dput(D) structure(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2.990484802, 3.005018792, 3.019552781, 3.03408677, 3.048620759, 3.063154749, 3.077688738, 3.092222727, 3.106756717, 3.121290706, 3.135824695, 3.150358684, 3.164892674, 3.179426663, 3.193960652, 3.208494642, 3.223028631, 3.23756262,

hi all i have another probably simple question. I have three variables say x, y and z. x and y are quite large and z is relative small. how can one plot the three variables on the same graph with two separate axis? (one for x and y and the other for z) e.g. x<-c(101,110,150,167,120) y<-c(120,135,175,95,200) z<-c(0.001, 0.15, 0.6, 0.8, 1) regards Allan

How do I plot using multiple(2) y-axes? I have two series that use the same x-data, but have very different scales. Appreciate any feedback, Per Bak

Hi All, I have checked everything I could find abot graphics, but still cannot solve the problem. Are there any ways to make a graph that plots two lines and two different y-axes, each of them has a scale that is related to the respective line. For example, y1 has a range 1:50 and y1 ranges 0:1. The x-axe is the same for both. Thank you in advance. --- Gregor Gawron

Hi to all, First of all, I prefer to tell that I am a R-newbie, so I apologize if this is a silly question (I have tried looking in the manuals, but without luck). I have two variables, y and z, that I want to plot against x in the same plot. I have done this before, using points() after plot(). But now the problem is that y and z are in different units of measurement, and their ranges are very

Dear all, I have run into a problem when running some code implemented in the Bioconductor panp-package (applied to my own expression data), whereby gene expression values of known true negative probesets (x) are interpolated onto present/absent p-values (y) between 0 and 1 using the *approxfun - function*{stats}; when I have used R version 2.8, everything had worked fine, however, after updating

Dear R users, I am trying to minimize the distance between my data points and theoretical gamma distribution over shape and scale parameters. the function "mde" from actuar package does it for empirical distribution function and theoretical gamma distribution. However, I would like to minimize the distance by using only the data between 0.1 and 0.9 quantiles. I cannot use ecdf in this

#See David Williams' book "Weighing the odds", p286 y <- c(1.21, 0.51, 0.14, 1.62, -0.8, 0.72, -1.71, 0.84, 0.02, -0.12) ybar <- mean(y) ylength <- length(y) ybarv <- rep(ybar, ylength) x <- 1:ylength plot(x,y,asp=1,xlab="position",ylab="ybar",type="n",ylim=c(-1,1)) segments(x[1], ybar, x[ylength], ybar) segments(x,ybarv,x,y)

I am working on a project where we start with start with 2 long, equal-length vectors, massage them in various ways, and end up with a function mapping one interval to another. I'll call that function "f1." The last step in R is to generate f1 as the value of the approxfun function. I would like to put f1 into a package, but without having the package redo the creation of

Hi all R users I would like to know how acess the coordinates of the ECDF object. I look for the example, in this part: ###################### print(ls.Fn12 <- ls(env= environment(Fn12))) ###################### but I do not know to extract the Y coordinate and put it in other variable. My objective is to make a plot and identify the points with labels. ############# Example by

Hi! I've noticed the ecdf() R code (R ver. 2.5.0) contains two call to sort: --- [R-code] --- ecdf <- function(x) x <- sort(x) n <- length(x) if (n < 1) stop("'x' must have 1 or more non-missing values") vals <- sort(unique(x)) rval <- approxfun(vals, cumsum(tabulate(match(x, vals)))/n, method

Hi people, I am new in this list and could not find a FAQ for it in particular, furthermore I could not find my question answered in the official R FAQ or docs. I have simply something like this: > f<-approxfun(data[,1],data[,2]) and f is: > f function (v) .C("R_approx", as.double(x), as.double(y), as.integer(n), xout = as.double(v), as.integer(length(v)),

Readers, According to the help '?approxfun', the function can be used to obtain the interpolated values. The following test was tried: > testinterpolation<-read.csv('test.csv',header=FALSE) > testinterpolation V1 V2 1 10 2 2 20 NA 3 30 5 4 40 7 5 50 NA 6 60 NA 7 70 2 8 80 6 9 90 9 10 100 NA >

Here is a simple example to show what I mean: f1 <- approxfun(1:3, 2:4) dump("f1", file="") which yields (in current development snapshot; other versions are conceptually equivalent) "f1" <- function (v) .C("approx", as.double(x), as.double(y), n, xout = as.double(v), length(v), as.integer(method), as.double(yleft),

I'm replying to R-devel, the mailing list which should be used to discuss R feature enhancements. >>>>> "Norman" == Norman Warthmann <norman at warthmann.com> >>>>> on Wed, 18 Jan 2006 11:33:22 +0100 writes: Norman> .......... Norman> Is there a specific reason why in the ecdf-function Norman> the variable

Dear R-devel, The following line crashes R > approx(1, 1, 0, method='const', rule=2, f=0, yleft=NULL, ties='ordered')$y Process R:2 exited abnormally with code 5 at Tue Jul 21 14:18:09 2009 > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 9.1 year

I would like you consider that the function ecdf could be extended in the following way to handle weights when computing Empirical distribution Functions. There exist particular cases that supports this kind of extension, see for example: Rao, C. R., 1997. Statistic and True. Putting chance to work. World Scientific Publishing. Cox, D. R., 1969. Some Sampling Problems in Technology. New

I've rolled up R-2.9.2.tar.gz a short while ago. This is a maintenance release and fixes a number of mostly minor issues. See the full list of changes below. You can get it from http://cran.r-project.org/src/base/R-2/R-2.9.2.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in due course (Duncan Murdoch is out of town, so Windows