similar to: R optim() function

Dear all, Just wondering if someone could help me out converting my code from a for() loop into a foreach() loop or using one of the apply() function. I have a very large dataset and so I'm hoping to make use of a parallel backend to speed up the processing time. I'm having trouble getting selecting three variables in the dataset to use in the foreach() loops. My for() loop code is:

Hi, I am learning R by reading R source code. Here is one question I have about the optim function in R. The context : In the optim.R, after all the prep steps, the main function call call is made via : .External2(C_optim, par, fn1, gr1, method, con, lower, upper). So, it seems to me, to follow what is going on from here, that I should read the optim function in \src\library\stats\src\optim.c

I am trying to estimate home range size using the plug-in method with kernel density estimation in the kernel smoothing (ks) package. Unless there is another way I am not familiar with, in order to calculate spatial area under the space I need to convert my kde () object into a spatial object somehow in order to calculate its spatial area. Could someone demonstrate how this might be done? --

I am attempting to perform some simple data manipulation on a large data set. I have a snippet of the whole data set, and my small snippet is 2GB in CSV. Is there a way I can read my csv, select a few columns, and write it to an output file in real time? This is what I do right now to a small test file: data <- read.csv('data.csv', header = FALSE) data_filter <- data[c(1,3,4)]

Hi, I'm trying to create a plot showing the density distribution of some shipping data. I like the look of violin plots, but my data is not continuous but rather binned and I want to make sure its binned nature (not smooth) is apparent in the final plot. So for example, I have the number of individuals per vessel, but rather than having the actual number of individuals I have data in the

Hi All, I apologise if this question has been answered before, but my background is a little different from most people using R, and the language we use seems to be different! I am trying to analyse some nuclear physics data, which consists of an ensemble of "energy" readings in a detector that, when binned, form a number of Gaussian shaped peaks superimposed on a varying background

How to use ggplot to do the binned quantile plots(one type of scatter plot)? Hi all, I have done scatter plot: plot(x, y). Now I wanted to do binned quantile plots... can ggplot2 help me? For example, we bin x data into 10 bins. For each bin, we draw the 10 deciles of the corresponding y data in that bin as points/dots. And then accross all bins, we would like to connect the corresponding

Hi, Standard correlations (Pearson's, Spearman's, Kendall's Tau) do not accurately reflect how closely the model (GAM) fits the data. I was told that the accuracy of the correlation can be improved using a root mean square deviation (RMSD) calculation on binned data. For example, let 'o' be the real, observed data and 'm' be the model data. I believe I can calculate

I've been trying to implement bivariate kernel density estimation. For data like mine, function "kde" from package "ks" with bandwidth matrix derived by function "Hscv" seems like a very good choice. Unfortunately, Hscv seems unmanageably slow except for very small sample sizes (up to a few hundred) and my sample sizes are quite large (up to a few thousand).

Hello, I'm load testing a new Asterisk 13 system (Debian Stretch, packaged asterisk). One system writes CDR though an ODBC connection to a local Postgres database over the LAN. When sending 50 new calls per second with SIPp, I'm seeing one system outputs : taskprocessor.c: The 'subm:cdr_engine-00000003' task processor queue reached 5000 scheduled tasks again. This [1] thread

Dear all, I have a binned data that looks like this: > dat (-1,9] (9,19] (19,29] (29,39] (39,49] (49,59] (59,69] (69,79] 10063374 79 16 4 3 4 4 3 (79,89] (89,99] 6 2 I tried to plot a histogram overlayed with curve. With the following snippet: library(lattice) pdf("myfile.pdf") hist(dat)

Dear all, I would like to know how can I create a binned column in a data.frame. The output that I would like is something like this: Start Binned_Start 1 0-5 2 0-5 6 5-10 8 5-10 13 10-15 ... Best regards João Fadista Ph.d. student UNIVERSITY OF AARHUS Faculty of Agricultural Sciences Dept. of Genetics and Biotechnology Blichers

Dear All, A simple question: packages like fitdistr should be ideal to analyze samples of data taken from a univariate distribution, but what if rather than the raw data of the observations you are given directly and only a histogram? I was thinking about generating artificially a set of data corresponding to the counts binned in the histogram, but this sounds too cumbersome. Another question is

Bump this to 44 bits for x86-32 and 52 bits for x86-64. Signed-off-by: Jan Beulich <jbeulich@novell.com> Index: 2007-02-07/tools/libxc/xc_linux_restore.c =================================================================== --- 2007-02-07.orig/tools/libxc/xc_linux_restore.c 2007-01-17 11:16:20.000000000 +0100 +++ 2007-02-07/tools/libxc/xc_linux_restore.c 2007-02-12 09:06:05.000000000 +0100

Is there anyway to plot a matrix using a 3d bar plot. Something like bar3 in matlab? The example in demo hist3d does a 3d barplot for binned data, but has anyone tried something for a simple matrix with spaces betwen bars and axis labels using matrix dimnames or 1,2,3? stages<-letters[1:3] A<-matrix(c( 0.21, 0.21,0.03, 0.55, 0.58, 0.09, 1.30, 1.35, 0.22), nrow=3, byrow=TRUE,

Dear R users, I have a matrix "final" which looks like this: final oSO4 oNO3 mSO4 mNO3 [1,] 3.3728 0.2110 1.9517421 1.01883602 [2,] 0.8249 0.0697 1.5970292 0.11368781 [3,] 0.2636 0.1004 0.6012445 0.24356332 [4,] 8.0072 0.3443 6.1016998 3.63207149 [5,] 13.5079 0.6593 12.4011068 1.55323386 [6,] 6.1293 0.1989 5.7620926 0.12884845 [7,] 0.6004 0.0661

Suppose I have a set of binned data, counts exceeding a series of arbitrary thresholds, a total N, a minimum and maximum, those sorts of things. Is there a "standard" method for estimating arbitrary quantiles from this? My initial thought is that the counts and min/max give me solutions at various points along the empirical cdf. As the data are roughly log-normal, I thought maybe I

Hello I need to fit a distribution to a histogram data set. I have read Ricci's guide to distribution fitting, and am ready to begin experimenting with the techniques it mentions, but I am uncertain how to get my data in the format he uses. My problem is that my data is binned. So for example my data is in the following format #lb ub count 0 1 4 1 2 7 2 3 2

Hi, I'm looking for a super-duper fast mean/sum binning implementation available in R, and before implementing z = binnedMeans(x y) in native code myself, does any one know of an existing function/package for this? I'm sure it already exists. So, given data (x,y) and B bins bx[1] < bx[2] < ... < bx[B] < bx[B+1], I'd like to calculate the binned means (or sums)

Hello, I am trying to create data frame subsets based on binned temperature data. I have code working to create the bins (d.1 and d.2), but it takes two steps, I was wondering if I could merge into one step. See Below d n year mo da hr t td tw rh kPa 1 1 1945 3 1 0 1.1 0.0 0.6 92 101.7 2 2 1945 3 1 1 2.8 -1.1 1.1 76 101.8 3 3 1945 3 1 2 2.2 -1.7 0.6 75 101.9 4 4