search for: logsplines

Full_Name: Emmanuel Charpentier Version: 1.2.0 OS: Linux 2.2.18 (Debian 2.2) Submission from: (NULL) (193.251.31.31) When trying "mass" installation of available packages, the package logspline does not compile, at least when installed through install.packages("logspline"). It seems that it is a small bug, such a missing #include <math.h> or -libm linking switch ?

Hi all, I've successfully compiled R-1.2 on a Linux box (Mandrake 7.1). However, when I installed packages from sources, I run into problems with the packages logspline and tseries. The error messages are as follows. Can anyone help? The compiler is gcc 2.95.3, if that helps. Andy ================================================ Installing source package `logspline' ... libs gcc

The following are now on CRAN: KernSmooth: version 2.2 of the code for Wand & Jones book on kernel smoothing. logspline: spline fits to log denisites, with automatic choice of smoothing. ppr: projection pursuit regression. rpart: recursive partitioning (CART-like) VR: Venables & Ripley libraries 5.3pl021 for 0.62.1 and in the devel section tree: a clone

The following are now on CRAN: KernSmooth: version 2.2 of the code for Wand & Jones book on kernel smoothing. logspline: spline fits to log denisites, with automatic choice of smoothing. ppr: projection pursuit regression. rpart: recursive partitioning (CART-like) VR: Venables & Ripley libraries 5.3pl021 for 0.62.1 and in the devel section tree: a clone

Dear List, I have a simple model as follows: x <- rnorm(500) library(logspline) fit <- logspline(x) n <- 1000000 y <- replicate(n, sum(rlogspline(rpois(1,10), fit))) # last line The problem I keep getting is R crashes when doing the last line. It seems to be fine if n is small, but not if n is 1000000. The message I keep getting is: "R for Windows GUI front-end has

Hi folks, I know that density function will give a estimated density for a give dataset. Now from that I want to have a percentage estimation for a certain range. For examle: > y = density(c(-20,rep(0,98),20)) > plot(y, xlim=c(-4,4)) Now if I want to know the percentage of data lying in (-20,2). Basically it should be the area of the curve from -20 to 2. Anybody knows a simple

So after about 4 hours struggling with Try and TryCatch I am throwing in the towel. I have a more complicated function that used logspline through iterative distributions and at some point the logspline doesnt function correctly for some subsets but is fine with others so I need to be able to identify when the error occurs and stop curtailing the distribution and I think this Try or TryCatch

Hi interested readers, I have a function that creates several functions within a loop and I would like them to be returned for further use as follows: Main.Function(df,...){ # df is a multivariate data funcList<-list(NULL) for (i in 1:ncol(df)){ temp<-logspline(df[,i],...) # logspline density estimate funcList[[i]]<-function(x){expression(temp,x)} } return(funcList) } I have tried

Can anyone recommend a good nonparametric density approach for data bounded (say between 0 and 1)? For example, using the basic Gaussian density approach doesn't generate a very realistic shape (nor should it): > set.seed(1) > dat <- rbeta(100, 1, 2) > plot(density(dat)) (note the area outside of 0/1) The data I have may be bimodal or have other odd properties (e.g. point mass

Dear R-list, I have made a simple kernel density estimation by x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10) kde <- density(x,n=100) Now I would like to know the estimated probability that a new observation falls into the interval 0<x<3. How can I integrate over the corresponding interval? In several R-packages for kernel density estimation I did not found a corresponding function. I

Dear all, This is a simple probability problem. I want to know, How to generate a normal distribution with mean=1, min=0.2 and max=0.8? I know how the generate a normal distribution of mean = 1 and sd = 1 and with 500 data point. rnorm(n=500, m=1, sd=1) But, I am confusing with how to generate a normal distribution with expected min and max. I expect to hear your directions. Thanks in

I am aware of the nice R package "logspline", which does smooth density estimation from interval-censored data (that is, values that are known to lie in a specified interval rather than known exactly). Function logspline.fit uses a maximum penalized likelihood method, with the penalty related to the number of knots used in a cubic regression-spline fit. I need to be able to do some

I using oldlogspline (from logspline package) to model data distributions, and having a problem. My data are search area sizes. They are based on circular search radii from random points to the nearest edge of the nearest grass tussock. Search area sizes are distributed from 0 (the random point intercepts a tussock) and upwards (as points are further from any tussocks). The density of all my

Hello, I'm writing some genetic simulations in R where I would like to place genes along a chromosome proportional to the density of markers in a given region. For example, a chromosome can be presented as a list of marker locations: Chr1<-c(0, 6.5, 17.5, 26.2, 30.5, 36.4, 44.8, 45.7, 47.8, 48.7, 49.2, 50.9, 52.9, 54.5, 56.5, 58.9, 61.2, 64.1) Where the numbers refer to the locations of

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

I've been trying to figure this out for a while, but my knowledge of R is obviously still too limited. The context is as follows: I have some time series, and I would like to estimate their densities, and then use the actual densities in a monte carlo simulation. Now, I can easily estimate the density using density(); I can write a random number generator to fit an arbitrary density

Dear R users, I would like to show the estimated density of a (0, 1) uniformly distributed random variable. The density curve, however, goes beyond 0 and 1 because of the kernel smoothing. Example: x = runif(10000) plot(density(x)) Is there a way to estimate the density curve strictly within (0, 1) and still use some sort of smoothing? Any help would be greatly appreciated. Best regards,

Hello, I need density function so that I can find expected value (using integration). I use density(): f= density(data) but f isn't a function and I can't get values and integrate it This is very urget, so please help. Greetings Peter -- View this message in context: http://r.789695.n4.nabble.com/density-function-tp4634563.html Sent from the R help mailing list archive at Nabble.com.

Hello, I wanted to know if there is a simple way of getting the inverse cdf for a KDE estimate of a density (using the ks or KernSmooth packages) in R ? The method I'm using now is to perform a numerical integration of the pdf to get the cdf and then doing a search for the desired probablity value, which is highly inefficient and very slow. Thanks, -fj [[alternative HTML version deleted]]

hi, this is more a statistical question than a R question. but I do want to know how to implement this in R. I have 10,000 data points. Is there any way to generate a empirical probablity distribution from it (the problem is that I do not know what exactly this distribution follows, normal, beta?). My ultimate goal is to generate addition 20,000 data point from this empirical distribution created