similar to: percentage from density()

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 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.

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/

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'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, there, I found density function?cannot work well on UNIX.R version 2.13.1 (2011-07-08), Platform: i386-pc-solaris2.10 (32-bit) Here is an example from R doucments which can work well on other version. plot(density(c(-20,rep(0,98),20)), xlim = c(-4,4))# But right now the estimated probablity =0 for all point. Do I need do something about float point? Thanks. Alex

Hi, guys, I'm using kernel "density" estimation. But how can I return to a density estimation for a fixed point? For example, b<-runif(1000,0,1) f<-density(b) How can I get the value of density(b) at b=0.5? Your help is extremely appreciated. Thanks. Jay -- View this message in context: http://r.789695.n4.nabble.com/help-in-density-estimation-tp2552264p2552264.html Sent

Hey, Does anyone know of a package in R that provides univariate kernel density estimation with boundary correction ? or how to easily extend an existing bivariate kernel density estimation function (e.g. lambdahat in the spatialkernel package) with boundary corrections to allow univariate density estimation? Thanks a lot, Steve B. -- View this message in context:

Hi, I am analysing a dataset containing genetic distances within and between species. I want to show a overlap of the distributions of the intra- and interspecific values; on a second graph I use a cut-off value to determine these boundaries. As the dataset contains >30 000 values, I would like to do this with a simple line rather than superimposed histograms. Hence, density plots. With

I'm plotting some trip length frequencies using the following code: plot( density(zTestData$Distance, weights=zTestData$Actual), xlim=c(0,10), main="Test TLFD", xlab="Distance", col=6 ) lines(density(zTestData$Distance, weights=zTestData$FlatWeight), col=2) lines(density(zTestData$Distance, weights=zTestData$BrdWeight ), col=3) which works fine except the

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, I have a vector 'data' of 58 probability values (bounded between 0 and 1) and want to draw a probability density function of these values. For this, I used the commands: data <- runif(58) a <- density(data, from=0, to=1) plot(a, type="l",lwd=3) But then, when I try to approximate the area under the plotted curve with the command: area <- sum(a$y)*(a$x[1]-a$y[2])

All the density estimators I've found in R seem to force the ends to go to zero. What can we do if we don't believe that, e.g., with something that might be a uniform distribution or a truncated normal with only observations above mu+sigma observed? The closest I could come to this was to artificially extend the numbers beyond the range, thereby forcing the density estimator

I am using density() to plot a density curves. However, one of my variables is truncated at zero, but has most of its density around zero. I would like to know how to plot this with the density function. The problem is that if I do this the regular way density(), values near zero automatically get a very low value because there are no observed values below zero. Furthermore there is some density

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, 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.

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

Hi the function density() does normally integrate to one - I've checked it and it works and I also read the previous threads. But I realised that it does not integrate to one if I use from, to or cut. My scenario: I simulated densities of a plants originating from an sseed source at distance zero. Therefore the density of the plants will be highest close to zero. Is there anything I can do

I'm trying to do something very simple... I wan to plot a histogram where the y axis represent the percentage of the total sample that each bin represents. I know how to plot a histogram with the counts and density... but can't find anything that gives me perenct of sample on the y axis. Any help is appriciated Below is the script I'm working with par(mfrow=c(1,2))