similar to: R-alpha: dump() / dput() -- fail for "environmental" and attributed functions

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

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 >

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)),

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

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

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

Petr Pikal said: > I am not really a R specialist but for this task I use function: and he pasted his code into the email. I reindented the code, and wrote a fragment to experiment with it. Here it is: --------------------------------------------------------------------------- plot.yy <- function(x, yright, yleft, yleftlim=NULL, yrightlim = NULL,

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

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

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

#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'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

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

Hi all, I recently switched to R version 2.6.2 (from 2.5.0 I was using) on Windows XP. I have a fair amount of previous data saved in the form of "approxfun" objects created by version 2.5.0, with code basically like this: > x <- rlnorm(100,10,2) > f <- approxfun(1:100,x) > f(5) [1] 313811.3 > save(f, file="mytest_approx.Rdata") Now on 2.6.2, when I try to

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

Below is the code that I am using in a much larger function. I would expect a bankfull measure at zero to be between 0.6 and 0.8 approxfun is returning 0.8136986. I am sure that I am missing something. measure_bkf <- (structure(list(measurment_num = c(0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2, 2.2, 2.4, 2.6, 2.8, 3, 3.2, 3.4), bankfull_depths_m = c(-0.48, -0.48, -0.42, -0.26, 0.58,

Last week I spent some time going through the Blue Book to figure out what we are missing and where we are different. (Results will come in subsequent emails.) As a byproduct, here is a list of the documentation problems I found. As soon as time and my knowledge of CVS allow, I'll try to fix some of these ... -k *********************************************************************** * The

I'm testing R version 2.7.0 on windows and there seems to be a compatibility issue with objects that were created by "approxfun" in older versions. As long as the objects were created in version 2.7.0 things work ok, but calling the interpolated functions from R version 2.0.1 causes this error: Error in .C("R_approx", as.double(x), as.double(y), as.integer(n), xout =