similar to: How to use result of approxfun in a package?

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 >

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-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'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 =

Full_Name: Martin Frith Version: R-2.0.0 OS: linux-gnu Submission from: (NULL) (134.160.83.73) I have large vectors containing 100,000 to 20,000,000 numbers. However, they only contain a few hundred *distinct* numbers (e.g. positive integers < 200). When I do ecdf(v), it either runs out of memory, or it succeeds, but when I plot the ecdf with postscript, the output is unnecessarily bloated

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, Suppose I have two vectors, not necessarily the same length (in fact, they usually are different lengths): y.1 that has increasing values between 0 and 1; y.2 that has decreasing values between 1.0 and 0. You can picture these as being supply (= y.1) and demand (= y.2) curves from economics. I typically plot these vectors on the same graph against a common x variable, which happens to

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

Dear All, My doubt about how to integrate a simple kernel density estimation goes on. I have seen the recent posts on integrate density estimation, which seem similar to my question. However, I haven't found a solution. I have made two simple kernel density estimation by: kde.1 <-density(x, bw=sd(x), kernel="gaussian")$y # x<- c(2,3,5,12) kde.2 <-density(y,

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,

Hi, all ecdf function (Empirical Cumulative Distribution Function) in "stats" package counts from smaller values to larger values. However, I want to draw it by counting from larger value to smaller values and I couldn't find options for this purpose. How can I draw ecdf or ecdf like graph by counting from larger values to smaller values. Thank you in advance. Hyunchul Kim

Hi, I've estimated a simple kernel density of a univariate variable with density(), but after I would like to find out the CDF at specific values. How can I do it? thanks for your help, with it I am very close to finish my first little bit more serious work in R, Viktor

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,

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

#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)

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 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

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

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random