similar to: Running Median and Mean

I am pretty pleased with baselines I fit to chromatograms using the runquantile() function in caTools(v1.6) when its probs parameter is set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-adjust any negative values to zero. But runquantile's

Dear all, you can find below my solution for sliding a window. Please find below the code for the two alternatives and the benchmarks.     install.packages('caTools') require(caTools) do_sliding_for_a_window_duty_cycle <- function(DataToAnalyse,  windowSize) {   data<-DataToAnalyse   out <- numeric()   elements<- numeric()   if (length(data[,1]) >= windowSize){       for

I have a Date x Stock (223 x 520) matrix of "trading volume". I can calculate a 5-day (past) average in about 1 second using: R> apply(vol, 1, filter, filter=c(0, rep(1/5,5)), sides=1) I would like to do the same with a 5-day median, e.g.: R> mymed <- function(x, n=5) { R> r <- rep(NA, length(x)) R> for (i in (n+1):length(x)) r[i] <- median(x[i-(1:n)]) R>

Is there a weighted median function out there similar to weighted.mean() but for medians? If not, I'll try implement or port it myself. The need for a weighted median came from the following optimization problem: x* = arg_x min (a|x| + sum_{k=1}^n |x - b_k|) where a : is a *positive* real scalar x : is a real scalar n : is an integer b_k: are negative and positive scalars

Dear Mr. Tuszynski, I would like to request what I believe would be a beneficial update / patch to the runmean() function in the caTools package. Consider the following R>> x = 1:100 R>> is.integer(x) [1] TRUE R>> library(caTools) R>> head(runmean(x, 5, alg="exact")) [1] 8.487983e-314 1.060998e-313 1.273197e-313 1.697597e-313 2.121996e-313 2.546395e-313

A new package "g.data" is available on CRAN, to create and maintain databases that work more like the S-Plus model. Here's the official Description for g.data (v1.2): Create and maintain delayed-data packages (DDP's). Data stored in a DDP are available on demand, but do not take up memory until requested. You attach a DDP with g.data.attach(), then read from it and assign

A new package "g.data" is available on CRAN, to create and maintain databases that work more like the S-Plus model. Here's the official Description for g.data (v1.2): Create and maintain delayed-data packages (DDP's). Data stored in a DDP are available on demand, but do not take up memory until requested. You attach a DDP with g.data.attach(), then read from it and assign

Hi, I'm very beginner for R but I think it is a time to start as it is very useful. I have a coverage read file (illusmp454merCbed) for whole genome ~ 450 Mbp. This is head of this file. Scaffold sca_position coverage Scaffold1 1 0 Scaffold1 2 0 Scaffold1 3 0 Scaffold1 4 0 Scaffold1 5 0 Scaffold1 6 0 Scaffold1 7 1 Scaffold1 8 3 Scaffold1 9 3 I would like to plot everage coverage for every 1

Another nicety may be handling network drives. I''ve been using a ruby script which manipulate using the "net use" command to map network drives, disconnect network drives, query to see what drives are connected, etc..., but it''d be sweet if this type of functionality was included in win32 package. Maybe usage like: nd = Win32::NetworkDrive.new(

I suspect this will have suboptimal performance since the TRUEs will get recycled. (Maybe there is, or could be, ALTREP, support for recycling) Hadley On Fri, Jun 8, 2018 at 10:16 AM, Berry, Charles <ccberry at ucsd.edu> wrote: > > >> On Jun 8, 2018, at 8:45 AM, Hadley Wickham <h.wickham at gmail.com> wrote: >> >> Hi all, >> >> Is there a better to

Hi, I asked this question 2 years ago, and would like to know if the answer has changed. In S-Plus, I build databases of many large objects. In any given analysis, I only need a few of those objects, but attach'ing the whole database is fine since objects are only read as needed. How can I do the same thing in R, without reading the entire database? One possibility is to treat

I''m attempting to run Puppet 3.2.3 on RHEL6 and am running into key problems. The keys seem to be exchanged, or at least the puppet master receives the key from the client: lib_puppet2.library.nd.edu|root no_ora /var/lib/puppet 1029$ puppet cert list --all + "puptest1.library.nd.edu" (SHA256)

Perdón por anticipado ante una pregunta sólo achacable a mi ignorancia en programación. Estoy creando un nuevo paquete con una estructura "decente", en vez de las chapuzas que hacía hasta ahora. Defino una función que ajusta unos datos a una distribución que podemos llamar ND. La sintaxis de esta función sería, de forma resumida: ND.fit<-function(x, start, ...){ ...

> On Jun 8, 2018, at 10:37 AM, Herv? Pag?s <hpages at fredhutch.org> wrote: > > Also the TRUEs cause problems if some dimensions are 0: > > > matrix(raw(0), nrow=5, ncol=0)[1:3 , TRUE] > Error in matrix(raw(0), nrow = 5, ncol = 0)[1:3, TRUE] : > (subscript) logical subscript too long OK. But this is easy enough to handle. > > H. > > On

Hi, First, let me thank Jaroslaw for making this survey. I find it quite illuminating. Now the questions: * the #1 solution below (based on cumsum) is numerically unstable. Specifically if you do the runmean on a positive vector you can easily get negative numbers due to rounding errors. Does anyone see a modification which is free of this deficiency? * is it possible to optimize the

Hello, I'm using pscl to run a hurdle model. Everything works great until I get to the point of making predictions. All of my "count" predictions are lower than my actual data, and lower than the "response" predictions, similar to the issue described here ( https://stat.ethz.ch/pipermail/r-help/2012-August/320426.html) and here (

Hi, I understand work is being done to improve read.table(), especially by Prof. Brian D. Ripley. I offer below a version that I wrote, in the hope some aspects of it may prove useful or at least inspire discussion. Be aware that my version differs in a couple fundamental ways that reflect my aversion to dataframes and factors. So it returns a list of vectors which are all character,

Someone has reported a problem with predict.coxph that I can't seem to solve. The underlying issue is with model.frame.coxph; the same issue is also found in lm so I'll use that for the example. -------------------------- > test <- data.frame(y = 1:10 + runif(10), x=1:10) > myfun <- function(formula, nd) { fit <- lm(formula, data=nd, model=FALSE)

Hi Evgeny, I just realized that if these are compile-time errors I can help investigate on my end. Do you have something I can use to reproduce? Cheers, Graham Yiu LLVM Compiler Development IBM Toronto Software Lab Office: (905) 413-4077 C2-707/8200/Markham Email: gyiu at ca.ibm.com From: Graham Yiu/Toronto/IBM To: Evgeny Astigeevich <Evgeny.Astigeevich at arm.com> Cc:

Hi all, Is there a better to way to subset the ROWs (in the sense of NROW) of an vector, matrix, data frame or array than this? subset_ROW <- function(x, i) { nd <- length(dim(x)) if (nd <= 1L) { x[i] } else { dims <- rep(list(quote(expr = )), nd - 1L) do.call(`[`, c(list(quote(x), quote(i)), dims, list(drop = FALSE))) } } subset_ROW(1:10, 4:6) #> [1] 4 5 6