similar to: boundary correction - univariate kernel density estimation

Assuming the days of raining during half a year of all states(provinces) of a country is normally distributed (mean=?, standard deviation=?) with sigma (?) equals to 2. We now have 10 data points here: 26.64, 30.65, 31.27, 33.04, 32.56, 29.10, 28.96, 26.44, 27.76, 32.27. Try to get the 95% level of CI for ?, using parametric Bootstrap method with bootstrap size B=8000. my code - what am i doing

Hi, Does anyone know which packages in R can deal with "univariate kernel density estimation with boundary correction" ? Thanks a lot. Ying-Chao Hung

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

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 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

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

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

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

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

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/

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 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.

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

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

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

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

Hi, I previously received help in extract data from a shapefile and now my question is about kernel density estimation. My objective is to have 3 kernel density plots; 2 for the each set of cases and the 3rd is the difference in kernel densities between the 2 sets of cases. Previously, I used the density function from the stats package, which worked but I wanted finer control of the bandwidth.

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