search for: approxfun

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 function f1. The value of approxfun is a function; for example, I have > f1 function (v) .C("R_approx", as.double(x), as.double(y), as.integer(n), xout = as.double(v), as.i...

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...

...gree to 0.5 degrees) to match that of a corresponding dataset. I have the data in both a data frame format (longitude column header values along the top with latitude row header values down the side) or column format (in the format latitude, longitude, value). I have used Google to determine 'approxfun' the most appropriate command to use for this purpose - I may well be wrong here though! Nevertheless, I've tried using it with the default arguments for the data frame (i.e. interp <- approxfun(dataset) ) but encounter the following errors: > interp <- approxfun(JanAv) Error in a...

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 to R 2.11.1., I got unexpected output (explained below). Please correct me here, but as far as I understand, the yleft and yright arguments set the extreme values of the interpolated y-values in...

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, 0.48, 0.47, 0.54, 0.5, 0.52, 0.52, 0.56, 0...

...ly treat/store byte-code compiled functions, as we have no problems with R-3.1.3, but do see an "Error in fct(x) : byte code version mismatch" with R-devel SVNrev r70532. Background: Starting from several x-y grids, in the sysdata.rda file of RobAStRDA, we store the results of calls to approxfun/splinefun to these grids from within a session with pkg RobAStRDA require()d. From pkg RobExtremes we then call these interpolating functions by means of a call (essentially) as: getFromNamespace(".RMXE", ns = "RobAStRDA")[["GEVFamily"]][["fun.N"]][[1]](1...

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 = as.double(v), : C symbol name "R_...

Platforms : SunOS and Linux, both using gcc 2.95.[23] R versions : 1.2.3 and "R-devel" I came accross this when trying to debug a somewhat sub-optimal behavior of ecdf() from package stepfun. What happens is that approxfun(*, ties = <function>) does not always return the same result for identical arguments. I tried "min", "max", and "mean" all of which are mentioned on the help page. >From looking at both R and C code, I wonder if the bug is not even deep inside where .C(......)...

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 reuse the saved object I get an error: > attach("myte...

...version; so please follow steps (1)--(6) below; I am not 100% sure whether this already gives you all information needed for this, but if not so please let me know. (1) create a minimal R-package "InterpolTry" with byte-compilation on in the DESCRIPTION file and with stats::approxfun imported in the NAMESPACE file (2) in an R session on R-devel do require(InterpolTry) x <- 1:100 y <- 1:100 fun <- approxfun(x,y) ## revise the next line accordingly to your local settings SrcRPathInterpolTry <- <path_to_(source-)R-folder_of_InterpolTry> RdaFile <- file....

...={ start.year <- 2008 end.year <- 2050 }) star.rating <- wedge$proto( star = c(4, 5, 8, 10), gain = c(0, .3, .5, .7), cost = c(0, 2100, 4000, 7500), star.rating <- function(., year) 6.0, setup = function(.){ .$cost.for.star <- approxfun(.$star, .$cost) .$gain.for.star <- approxfun(.$star, .$gain) }, test = function(., year) { gs <- .$with(gain.for.star)(.$star.rating(year)) } ) And a function to create and modify 'instances': create.star.rating <- function(switch....

...;t work so well for distributions that have the great majority of > > the probability mass concentrated in a small fraction of the sample > > space. I was hoping for something that would retain the > > adaptability of integrate(). > > Yesterday, I've suggested to use approxfun(). Did you consider my > suggestion? Below is an example. > > N <- 500 > x <- rexp(N) > y <- rank(x)/(N+1) > empCDF <- approxfun(x,y) > xvals <- seq(0,4,.01) > plot(xvals,empCDF(xvals),type="l", > xlab="Quantile",ylab="Cumulative D...

...ollow steps (1)--(6) below; I am not 100% sure whether this already gives > you all information needed for this, but if not so please let me know. > > (1) create a minimal R-package "InterpolTry" > with byte-compilation on in the DESCRIPTION file > and with stats::approxfun imported in the NAMESPACE file > > (2) in an R session on R-devel do > > require(InterpolTry) > x <- 1:100 > y <- 1:100 > fun <- approxfun(x,y) > ## revise the next line accordingly to your local settings > SrcRPathInterpolTry <- <path_to_(source-)R-folder_...

...e generated a profile likelihood for a parameter (x) and am trying to get 95% confidence limits by calculating the two points where the log likelihood (LogL) is 2 units less than the maximum LogL. I would like to do this by linear interpolation and so I have been trying to use the function approxfun which allows me to get a function to calculate LogL for any value of x within the range of x values. My data frame can be entered as follows: dat = data.frame(x = c(0.00000, 0.00010, 0.00100, 0.01000, 0.02404, 0.02404, 0.07603, 0.07603, 0.10000, 0.12021, 0.12021, 0.15146, 0.15146, 0.19083,...

...led > functions, as we have no problems with R-3.1.3, but do see an "Error in fct(x) : byte code > version mismatch" with R-devel SVNrev r70532. > > Background: > Starting from several x-y grids, in the sysdata.rda file of RobAStRDA, we store the results > of calls to approxfun/splinefun to these grids from within a session with pkg RobAStRDA > require()d. From pkg RobExtremes we then call these interpolating functions by means of > a call (essentially) as: > > getFromNamespace(".RMXE", ns = "RobAStRDA")[["GEVFamily"]][["...

I'm using area() to evaluate the area under 2 functions (defined using approxfun()) and the result is in contradiction with visual inspection (please see: http://sites.google.com/site/eospansite/dummy/problem_area.jpg ) The area under the red curve should be larger than the one under the black curve, but I'm geting the opposite result using area(). This is what I'm do...

This is a subset of a much larger dataframe. I would like to be able to automate finding the pair of x, y coordinates where the line crosses zero agian x <- (structure(list(bankfull_depths_m = c(0, 0.17, 0.38, 0.37, 0.36, 0.39, 0.47, 0.48, 0.19, 0.05, -0.05, -0.09), measurment_num_m = c(0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2, 2.2, 2.4)), .Names = c("bankfull_depths_m",

Hi R users, I am a new user, still learning basics of R. Is there anyway to extract y (or x) value for a known x (or y) value from ecdf (empirical cumulative distribution function) curve? Thanks in advance. Mano. [[alternative HTML version deleted]]

...t have the great > majority of > > > the probability mass concentrated in a small fraction of > the sample > > > space. I was hoping for something that would retain the > > > adaptability of integrate(). > > > > Yesterday, I've suggested to use approxfun(). Did you consider my > > suggestion? Below is an example. > > > > N <- 500 > > x <- rexp(N) > > y <- rank(x)/(N+1) > > empCDF <- approxfun(x,y) > > xvals <- seq(0,4,.01) > > plot(xvals,empCDF(xvals),type="l", > > xlab...

...le_grob)) } else { ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...)) } }) create_quantile_segment_frame <- function (data, draw_quantiles, split = FALSE, grp = NULL) { dens <- cumsum(data$density)/sum(data$density) ecdf <- stats::approxfun(dens, data$y) ys <- ecdf(draw_quantiles) violin.xminvs <- (stats::approxfun(data$y, data$xminv))(ys) violin.xmaxvs <- (stats::approxfun(data$y, data$xmaxv))(ys) violin.xs <- (stats::approxfun(data$y, data$x))(ys) if (grp %% 2 == 0) { data.frame(x = ggplot2:::interleave(vio...