similar to: converting a for loop into a foreach loop

Hi R-Help, I am using R to do functional outlier detection (using PCA to reduce to 2 dimensions - the functional boxplot methodology used in the Rainbow package), and using Hscv.diag function to calculate the bandwidth matrix where this line of code is run: result <- optim(diag(Hstart), scv.mat.temp, method = "Nelder-Mead", control = list(trace = as.numeric(verbose))) Within the

I would like to include this in a package. The S3 methods on R CMD check says * checking S3 generic/method consistency ... WARNING window: function(x, ...) window.chron: function(data, day1, hour1, day2, hour2, ...) See section 'Generic functions and methods' of the 'Writing R Extensions' manual. I have looked and can not figure it out. This function is for convience. What

thanks to your guys help I am closer to solving my problem but I have some small problem. So let's say I start with >data number day hour 1 17 10 2 17 11 3 17 6 4 18 4 5 18 10 6 19 8 7 19 8 I want to split to odd days, which I am able to do, I call this object frames, which looks like: > frames $`1` c1 day1 hour1 1 1 17 10 2 2 17 11 3 3 17 6 $`2` c1 day1

I have a series of tables, one for each environment indicating a date (row) and a sample at each hour of the day (0 to 23) Test1 Table: Date,Hour1,Hour2,...Hour23 1/1/10,123,123,...,123 I would like to model this as a time series but how can I translate the table into a list such that I can get: 1/1/10 00:00, 123 1/1/10 01:00, 123 1/1/10 02:00, 123 ... 1/1/10 23:00, 123 Any suggestions on how

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)]

Full_Name: Liming Liang Version: 1.8.1 OS: windows2000 professional Submission from: (NULL) (67.172.81.139) I was looking at the variable 'mpg' of the data file 'mtcars' and make a stem plot, the following is the commend I entered. The stem plot shows the largest observation is 32.9 but actually in the data the largest observation is 33.9, here might be a problem. >

Hi All, Wanted to add some more information ,regarding my problem. configuration of teh OS and R:--- Linux 2.6.18-6-686 > R.Version() $platform [1] "i486-pc-linux-gnu" $arch [1] "i486" $os [1] "linux-gnu" $system [1] "i486, linux-gnu" $status [1] "Patched" $major [1] "2" $minor [1] "4.0" $year [1] "2006"

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

I am working with a longitudinal data set in the long format. This data set has three observations per grade level per year. Here are the first 10 rows of the data frame: >tenn.dat[1:10,] year schid type grade gain se new cohort 6 2001 100005 5 4 33.1 3.5 4 3 7 2002 100005 5 4 33.9 3.9 4 2 8 2003 100005 5 4 32.3 4.2 4 1 10 2001 100005

Dear R-helpers, thanks for yesterday's speeding-up tip. Here is my next query: I have lots of polygons (not necessarily convex ones, and they never have holes) given by x,y coordinates. I want to get the polygon that is the union of these polygons. This is my current method, but I am hoping there is a faster method (up to thousands of polygons, each with ca. 40 xy points). Example:

Hi all I love Sweave; use it all the time. But I recently received a new computer, and ever since I have had a problem I've never seen before. For example, I place the following in my Snw file: <<>>= sms <- read.table("http://www.sci.usq.edu.au/staff/dunn/Datasets/applications/popular/smsspeed.dat", header=TRUE) attach(sms) sms.lm <- lm( Time ~ Age*Phone,

Current sources (git-b1b6caf) were compiled with GCC 4.8.2 and GCC 4.9.0 with various -msseN options (the default is -msse2). Then I took two WAV files (one is 16-bit and the other is 24-bit) and compressed them using best compression mode. The results are in the table below. (please remember that the resulting value is an encoding time, not encoding speed) CPU: Intel Core i7 950 (up to SSE4.2)

Hi everyone, I've run into a bit of strange problem with implicit vs explicit printing and the call stack. I've included an example at the bottom of this email. The basic problem is that I have an S3 object with a print method. When the object is implicitly printed (ie. typed directly into the console) the function arguments in the call stack are exploded out to their actual values,

  Hi r-users,   I would like to use gev and my data (annual rainfall ) is as follows:   > head(dat,20) A B C D E F G H I J 1 45.1 41.5 58.5 50.1 46.0 49.1 37.7 49.1 59.8 54.0 2 50.3 39.8 49.4 56.4 49.4 48.8 42.1 49.8 49.4 58.3 3 41.7 39.3 44.6 39.1 35.7 41.5 40.8 40.8 38.5 45.6 4 50.7 33.9 48.4 28.2 35.5 39.1 61.4 17.0 30.7 38.3 5 39.3 30.6 46.9 23.8 25.8

I asked this before and I am going to try again in more applied terms. I am trying to use R to extract variance components for a two-factor random effects model with both factors crossed. It would also be nice to generate some confidence intervals as well. For example, a data set using SAS Proc Mixed is below followed by the four variance component estimates and the respective confidence

Dear all, I am trying to validate a model by comparing simulated output values against observed values. I have produced a simple X-y scatter plot with a 1:1 line, so that the closer the points fall to this line, the better the 'fit' between the modelled data and the observation data. I am now attempting to quantify the strength of this fit by using a statistical test in R. I am no

2017-10-10 8:25 GMT+02:00 Karan Sandha <ksandha at redhat.com>: > Hi Gandalf, > > We have multiple tuning to do for small-files which decrease the time for > negative lookups , meta-data caching, parallel readdir. Bumping the server > and client event threads will help you out in increasing the small file > performance. > > gluster v set <vol-name> group

CentOS Errata and Enhancement Advisory 2018:2675 Upstream details at : https://access.redhat.com/errata/RHEA-2018:2675 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) i386: 6d40eac28340c2041aea1775a75ec383b7f780ca639adfe80af7e4bf792f87ef microcode_ctl-1.17-33.9.el6_10.i686.rpm x86_64: