similar to: Unwanted behaviour of bw.nrd: sometimes, zero is returned as a valid bandwidth

Hi all, I have a question regarding the density function which gives the kernel density estimator. I want to decide the bandwidth when using gaussian kernel, given a set of observations. I am not familiar with different methods for bandwidth determination. Below are the different ways in R on deciding the bandwidth. Can anyone give an idea on which ones are preferred. Also, how can I take

Hello, JIT compiler interferes with internal R completions: compiler::enableJIT(2) utils:::functionArgs("density", '') gives: utils:::functionArgs("density", '') Note: no visible global function definition for 'bw.nrd0' Note: no visible global function definition for 'bw.nrd' Note: no visible global function definition for 'bw.ucv'

Dear R Users, Before running ksmooth( ), a suitable bandwidth selection is needed. I use some functions for this task and receive these results for my data: width.SJ(y,nb=100,method="ste") : 40.25 bcv(y,nb=100) : 40.53 ucv(y) : 41.26 bandwidth.nrd(y) : 45.43 After implementing the function ksmooth(x,y, bandwidth= each of abovementioned bandwidths), I have some NAs

I am using the following commands for plotting kernel density for three kinds of crops density(s22$Net_income_Total.1, bw="nrd0",adjust=1, kernel=c("gaussian"))->t plot(t, xlim=c(-30000,40000), main="Net Income Distribution", axes=F, ylim=c(0,0.00035). xlab="Value in Rupees") par(new=T) density(s33$Net_income_Total.1, bw="nrd0",adjust=1,

Thanks for the report. The attached patch removes them from the page. OK? On 8 May 2013 11:34, Kostyrka (External user) Andreas <Andreas.Kostyrka at kapsch.net> wrote: > Not sure, but it seems the page contains a number of out-of-date entries: > > > > Pypy => pypy.org (link stale) plus: there is no llvm backend for pypy at the > moment (although LLVM backends have been

Dear All, (this msg is a statistics/computing question to the list) I'm trying to implement a modern-version of a (classic) "several-step protocol" in Fishery Biology (due to Bhattacharya, 1967): analysis of length-frequency distribution of fish larvae to id cohorts and later estimate growh rates! I've trouble with the 1st step: using kernel density estimation (KDE) function

The following is from version 1.6.2 of R under Windows, or 1.6.1 under Mac OSX/X11 > density(1) Error in if (!(lo <- min(hi, IQR(x)/1.34))) (lo <- hi) || (lo <- abs(x[1])) || : missing value where logical needed I am not sure how this should be handled. I encountered it in connection with densityplot(). In that connection, it might be enough to modify density() so that it

Dear wizaRds, I use the MASS::kde2d function to estimate density of the two first principal components. I do that to have a graphic visualisation of a "group structure" in my dataset. So far, no problem. But i would like to estimate that density using weights according to the COS?? values that tells me if my observation is well represented on the factorial plan 1-2. I would like to

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

Dear All, I have a variable q which is a vector of 1000 simulated positive values; that is I generated 1000 samples from the pareto distribution, from each sample I calculated the value of q ( a certain fn in the sample observations), and thus I was left with 1000 values of q and I don't know the distribution of q. Hence, I used the given code for kernel density estimation to estimate the

For the hist estimate >par(mex=1.3) >dens<-density(q) >options(scipen=4) > ylim<-range(dens$y) > h<-hist(q,breaks="scott",freq=FALSE,probability=TRUE, +? right=FALSE,xlim=c(9000,16000),ylim=ylim,main="Histogram of q(scott)") > lines(dens) >box() ? For the kernel estimate>options(scipen=4) > d <- density(q, bw =

Dear all, This is the first time I am posting to the r-devel list. On StackOverflow, they suggested that the strange behaviour of integrate() was more bug-like. I am providing a short version of the question (full one with plots: https://stackoverflow.com/q/55639401). Suppose one wants integrate a function that is just a product of two density functions (like gamma). The support of the

Dear all, I have been implementing some bootstrap-related methods, and came across this theoretically undesirable behaviour in the computation of bootstrap quantiles. The manual says: ?Interpolation on the normal quantile scale is used when a non-integer order statistic is required.? Theoretically, when R=999 and (R+1)*alpha is integer, then, the calculations of the 95% CI should never contain

Dear R helpers I am having recovery rates as given below and I am trying to estimate the Loss Given Default (LGD) and for this I am using Kernel Density estimation method. recovery_rates = c(0.61,0.12,0.10,0.68,0.87,0.19,0.84,0.81,0.87,0.54,0.08,0.65,0.91, 0.56,0.52,0.30,0.41,0.24,0.66,0.35,0.36,0.64,0.55,0.43,0.36,0.28,0.89,0.11,0.23,0.07,

$B!g!g!g!g!y!g!g!g!g!g!y!g!g!g!g!g!g!g!g!g!y!g!g!g!g!g!y!g!g!g!g!g(B $B!c(BNO.I don't veceive your mail$B!d"M!!(Becchi_daisuki69@yahoo.com.au $B!c:#8e!"<u?.$r5qH]$9$k>l9g$O!d"M(Bsweets85433@yahoo.com.au $B!g!g!g!g!y!g!g!g!g!g!y!g!g!g!g!g!g!g!g!g!y!g!g!g!g!g!y!g!g!g!g!g(B

Hello All, When I use bw.SJ (based on Sheather & Jones, 1991) in R to estimate the bandwidth for a highly skewed data, I get the following message: "sample is too sparse to find TD". I played around with the parameters such as no. of bins (nb), lower, upper (range over which to minimize) to no avail. My sample size is 50,000. Can anyone tell me what this means and of some

I need a "complete" list of "reliable" services for repeating the signal of a station to the listeners (live radio). The situation is that a station will be doing live radio and has a little DSL connection and external bandwith will be required. I don't mind if it costs much or it is cheap, I now just want to join information to say my customer: You have all this coices,

Original Message: ----------------- From: Stef Coene stef.coene@docum.org On Saturday 19 April 2003 12:10, GoMi wrote: >> Stef, if you are interested in working around this problem, maybe we >could >> meet in a chat room, and you can login to my box and see what happens, >> interested? >Yes, but not now. But I can check it out myself. >How do this DAP works? Do they

Hello, I need to limit the bandwidth to 1Mbit per user IP (and never give more) and inside of each 1Mbit prioritize minimum-delay traffic (video stream from specific servers, so I mangle TOS for packets from those). The problem is I need this for *thousands* of users. I am guessing thousands of classes are needed, but the question is, is this feasible? Could one machine ever handle this? How

Hi, i have a question if linux is able to do the same thing that commercial wan simulators do (which cost 7000 $ or more). Basicly i want to use the following setup. Linux Router Machine 192.168.0.1 192.168.1.1 Eth0 192.168.10.1 Eth5 192.168.2.1 Eth1 192.168.100.1 WEB Server. 192.168.3.1 Eth2