similar to: Bandwidth - Kernel Density Estimation

Hi Sunmanagers I still have a problem with my samba printing mechanism. When I log on some windows machines it will not print. I have found out that when I print from some machines while the print queu is stopped then the jobs are saved there as root. When I print from other machines they are saved as my name. I can print from dos by using echo something > \\server\printer. I can print with

Dear R helpers, I am using Beta distribution to generate the random no.s (recovery rates in my example). However, each time I need to save these random no.s in a csv format. To distinguish different csv files, one way I thought was use of Sys.time in the file name. My code is as follows - # My code rr = rbeta(25, 6.14, 8.12) lgd = 1 - mean(rr) write.csv(data.frame(recovery_rates = rr), file =

Hi, I'm relatively new to R and R development, so please forgive me for any obvious errors. What I am trying to do is use the command dpik within the package KernSmooth to estimate bandwidth parameters for GPS telemetry data. I have been able to get this to work on a case by case basis without any problem, but would like to extend this so that I can batch process many different animals for

Hi, I have two separate questions relating to the KernSmooth package. I am using the dpik function from the KernSmooth package and receive the error Warning message: In kappam * Gcounts : longer object length is not a multiple of shorter object length I saw an earlier post , but the issue was using the bkde fxn and the person appeared to be using too small of a bandwidth.

Thank you for your reply. I know the x in dpik() means the vector. But I don't know how to import into c() with a huge metadata (>1000). Following is my some try, and the h is: [1] 0.001180569, which seems to be feasible.

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,

Hi there and thanks in advance. Nowadays I am working on the plug-in bandwidth selection with R. Firstly, my 1010 data is the return rate from Yahoo Finance. Secondly, my code is following: > r=read.table("/Users/user/Desktop/research/a.txt",sep=",",header=TRUE) > x<-r[8:1010,] > library(KernSmooth) >

We just popped 2.2.6rc2 on to see if we could resolve a few printing issues, and now I can't seem to do the printer sharing on any printers on the server... In [global]: workgroup = CORP netbios name = NEWLUCY security = DOMAIN encrypt passwords = Yes update encrypted = Yes password server = LUNA pam password change = Yes

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

My mistake sir. I was literally engrossed in my stupid logic, and while doing so, overlooked the simple and very effective solution you had offered. Sorry once again sir and will certainly try to be very careful in future. Thanks again and have a great weekend sir. Regards Amelia --- On Fri, 7/1/11, Petr PIKAL <petr.pikal@precheza.cz> wrote: From: Petr PIKAL

Dear all, Sorry if I am posting to the wrong place, but I could not find the link for registration on the bug tracker, that?s why I am writing here. I think there is inconsistency between two R functions from the stats package, bw.nrd0 and bw.nrd. Consider the following vector: D <- c(0, 1, 1, 1, 1) bw.nrd(D) returns zero bandwidth for this object even without a warning. Considering the

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

Hi I hope this is going to the right place. I am trying to write a program which uses KernSmooth library to estimate mutual information between two time series at various different lags. At the moment it’s producing negative values, which is supposed to be impossible (something is fishy). I am summing across one row of the matrix to get p(value is in bin x) and summing across the columns to get

Dear R-help-list, The following is R function I wrote for computing multi-dimensional kernel density. I am seeking R experts who can make the code to run faster, 50 times faster ideally. Specifically, for function kernel.estimate = function(points, bw), the argument points is a d by n matrix as the n points in the d-dimensional space, bw is the bandwidth. The function will compute the kernel

Hi, Because I'm new to R and trying to move off of SPSS, I have another question. I downloaded the "Foreign" package which I understand reads in various datasets from programs such as SPSS and SAS. There isn't much documentation that I can find regarding the specifics of this in the online docs, but I assume that it is like read.table. So, here is what I wrote and here is

Hi, I am using the KernSmooth package to estimate nonparametrically bivariate density functions. However, it seems that the bandwidths (one for each co-ordinate direction) have to be selected manually. This does not apply for the univariate case, for which dpik (included in KernSmooth) uses up-to-date plug-in rules. Does anyone know about a package, or function, which estimates bandwidths

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 sir, I am extremely sorry for messing up the logic asking for help w.r.t. my earlier mails   I have tried to explain below what I am looking for.     I have a database (say, currency_rates) storing datewise currency exchange rates with some base currency XYZ.   currency_rates <- data.frame(date = c("12/31/2010", "12/30/2010", "12/29/2010",

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