similar to: Cumulative density (kernel smoothing)

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.

Hi all, is there any package in R capable of smooth approximation of CDF basing on given sample? (Thus, I am not speaking about ecdf) In particular, I expect very much that the approximation should subject to the property: f(x0)<=f(x1) for x0<x1, where x0 and x1 belong to range of the sample given. Polynomial approximation could be OK for me as well. P.S.

Dear all, I'm trying to estimate the parameters of a lognormal distribution fitted from some data. The tricky thing is that my data represent the time at which I recorded certain events. However, in many cases I don't really know when the event happened. I' only know the time at which I recorded it as already happened. Therefore I want to fit the lognormal from the cumulative

I have been using the ECDF function in the Hmisc package to produce cumulative distribution function plots. The problem is that for small datasets the steps "look bad" (not my characterization but from the client). Is there a way to get the same information but smoothed? I have tried the densityplot (lattice), which gives a smoothed line, but this does not give the cumulative density.

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

Hi list, this is my second try for first post on this list (I tried to post via email and nothing appeared in my email-inbox, so now I try to use the nabble-web-interface) - I hope that you will only have to read one post in your inbox! Okay, my question ... I was able to plot a histogram and add the density()-line to this plot. I was able to plot a cumulative form of this histogram. Yet, I was

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

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

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'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, Do any packages implement density estimation in a modelling framework? I want to be able to do something like: dmodel <- density(~ a + b, data = mydata) predict(dmodel, newdata) This isn't how sm or KernSmooth or base density estimation works. Are there other packages that do density estimation? Or is there some reason that this is a bad idea. Hadley -- http://had.co.nz/

http://r.789695.n4.nabble.com/file/n3567636/ecdfs.jpg ecdfs.jpg http://r.789695.n4.nabble.com/file/n3567636/ecdf_curve.gif ecdf_curve.gif Hello, I have generated a plot of two empirical CDFs (attachment 1). As a result, they are stepwise when plotted. The following code was used: > plot(ecdf(mut), do.points=FALSE, verticals=TRUE, xlim=range(mut, non), > col="red") >

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,

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

hi all: ecdf() is the empirical cumulative distribution function, but I need the INVERSE cumulative distribution. how can I calculate it? thank you in advance /salva

Hi, I have two variables ranging both from 0 to 1 (n=500 each). Now I am interested in plotting them both in one plot (using ggplot2). So far I used ecdf() (from an example I found with google) to get values for the cumulatice distribution function which gives a relative curve. I also want to do the same plot but using absolute cumulative values instead of relative. Can that be done with ecdf or

Hi, all ecdf function (Empirical Cumulative Distribution Function) in "stats" package counts from smaller values to larger values. However, I want to draw it by counting from larger value to smaller values and I couldn't find options for this purpose. How can I draw ecdf or ecdf like graph by counting from larger values to smaller values. Thank you in advance. Hyunchul Kim

Hello R-Help, did somebody ever implement some hazard smoothing routine in R (that allows, say, some group comparison as for the Kaplan-Meier curves.) As far as I can tell it is not in survival5 package. Thanks, -Yudi- -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Dear list, I would like to plot the empirical cumulative distribution of the time needed by a treatment to attain a certain goal. A number of experiments is run with a strict time limit. In some experiments the goal is attained before the time limit, in other experiments time expires before the goal is attained. The situation is very similar to survivial analysis with censored data. I tryed

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