similar to: about lscv

Hi,  I am looking to perform a kernel density analysis in R, following the script that a friend of mine is using successfully. However, unlike for her, when I enter >kud<-kernelUD(data, h="LSCV"), R crashes and closes every time. This does not happen when I perform a different kernel analysis (>kud<-kernelUD(data)).. I have tried to un-install and re-install R, but it still

R List, I am seeking help in the area of kernel smoothing. I am attempting to use LSCV values to conduct 2d and weighted 2d kernel smoothing (imported from an ArcMap shapefile), and have been unable to find tools to conduct such analysis in R. My biggest problem is determining the LSCV value and finding a tool that does weighted smoothing, as there are obviously many different packages that will

Hi I have a problem with passing line and symbol parameters to user defined panel functions I had a look at the archives and created a panel function on what was shown and on panel.loess. I could not to get panel.locfit to work for what I intend it for. There is another layer to work with before success as lp() is called from locfit. xx <- structure(list(Farm = c("A",

Hello to everybody, I need to know the Smoothing Parameter to obtain Home Range of an animal through the Area Kernel. I have 200 locations with x and y. How can I obtain the Smoothing Parameter with R for LSCV, CV and Href method??? Thank you. -- View this message in context: http://www.nabble.com/Smoothing-Parameter-Kernel-tp26037651p26037651.html Sent from the R help mailing list

Hello, I have a data x with normal (or very close to normal) distribution, I can plot a density distribution with density(x,...). My question is is there any way to calculate an area under this distribution (=probability) for particular range of x values, let's say for x from 0 to 2? I was not able to find any kind of simple procedure to do this. Thanks in advance for your help, Evgeny.

http://www.cs.purdue.edu/homes/jv/pubs/ecoop12.pdf A new paper out on R the language -- I'm not all the way through it but it's been an interesting read so far. Thought it might be of interest to the list. Michael Weylandt [[alternative HTML version deleted]]

Dear All, I have a bunch of data points as follows: x  100 y  200 z  300 ... where 100, 200, 300 are the values. I would like to know the distribution of my data? how can I fit my data into a distribution? Thanks a lot, Andra [[alternative HTML version deleted]]

Hi List, I'm having problems installing certain packages. When I try to install fields, I get the output below. I have highlighted the what I perceive as the first error (spam seems to be a dependency of fields). Can I assume this is causing dependency problems for fields, which fails to install at the end, or is there a deeper problem? I run Ubuntu Karmic Koala on R 2.9.2. 2>

Dear all, I'm trying to construct confidence intervals for a LOWESS estimation (by not using bootstrapping). I have checked previous posts and other material online and I understand that the main procedure is: my.count<- seq(...) fit<- loess (y ~ x, data=z) pred<- pred(fit, my.count, se=TRUE) and then the plotting. However, it's not working; as confidence

Hello, I am brand new in this site as well as in R. Please accept my early apology if it's not the right place to ask such a question. I have the following equation: b=[2(1-a)(1+2a)^0.5]/(1+3a) How can I solve the above equation for 'a' using R? Thanks in advance. KD [[alternative HTML version deleted]]

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

Hi all I have a function of log defined by y = log(1- exp(-a)), when exp(-a) is greater, 1, it produce NaN. How can I remove this in R? [[alternative HTML version deleted]]

My intention is to give a presentation about R programming language for software developers. I would like to ask, what are the things that make R different from other programming languages? What are the specific cases where Java/C#/Python developer might say "Wow, that was neat!"? What are the things that are easy in R, but very difficult in other programming languages (like Java)?

Hi ilai Thank you for your suggestions. I do not know what happened yesterday I must have omitted a few changes out in going from R to email and apologies for the double posting - I had troubles sending it as my ISP gave a message of not being connected for email but was for the web I was trying to get panel.Locfit to work in a number of situations. 1. Conditioned by Farm (3 panels) with 2

Dear R-devel, I'm working on Prof. Loader's new version of locfit to try to get it pass R CMD check. I'm almost there, but I have a problem with some Rd files that I hope some one can help me resolve. Here's an example: In the package there's a function called locfit.censor(). This function can be used in a few different ways: locfit.censor(x, y, cens, ...)

Dear list members, I am trying to understand this output from the smoothing package locfit (1.5-4, running on R 2.8.1 on Windows Vista 64 bit). # sample code x<-1:100 y<-rnorm(100) fit<-locfit(y~x,family="gaussian") #default parameters are fine plot(fit,band="global") #plot seems "reasonable", confidence bands use a global estimate of variance

Dear contributors I have tried this experiment: x<-c() for (i in 1:12){ x[i]<-list(cbind(x1[i],x2[i])) #this is a list of 12 couples of time series I am using to perform a test } # that compares them 2 by 2 # ################# #trace statistic test<-data.frame() cval<-array( , dim=c(2,3,12)) for (i in 2:12){ for (k in 1:2){ for (j in 1:3){ result[k,j,i]<-

Dear R-users, I intend to do a spatial analysis on the genetic structuring within a population. For this I had thought to prepare a kernel density estimate map showing the spatial distribution of individuals, while incorporating the genetic distances among individuals. I have a dataset of locations of N unique individuals (XY-coordinates) and an NxN matrix with the genetic distances given as a

Hi, I'm trying to work through the examples and code in Loader's LOCAL REGRESSION AND LIKELIHOOD, and have run into a problem with the code for one sided smoothing and change point analysis (p. 110-112). The code, after loading locfit: midp<-(1945:1988)+0.5 fitl<-locfit(thickness~left(year), data=penny, alpha=c(0,10), deg=1, ev=midp)

Hi, I have a Q on locfit, E.g., I have an input data set of the form {(x, y, v)} , (x, y) is the location in a 2D space, v is the value at that location. I am wondering for the output of locfit(), if a given point (x, y) is in the input data set, is the value at (x, y) going to be the exactly same as the input? or it depends on the local fitting function? does my Q make sense? Thanks a lot in