similar to: density at particular values

Hello, I want to draw a histogram of the mean of sample observations drawn from multivariate t distribution. I am getting the following error corresponding to the code I used. Though I am getting the graph, but I am curious to know the warning message. Warning messages: 1: In if (freq) x$counts else { : the condition has length > 1 and only the first element will be used 2: In if (!freq)

Hello, my name is Carsten. This ist my first post to R-help mailing list. I estimate densities with the function "density" out of the package "stats". A simplified example: #generation of test data n=10 z = rnorm(n) #density estimation f=density(z,kernel="epanechnikov",n=n) #evaluation print(f$y[5]) Here I can only evaluate the estimation at given

There were some questions about hist() a couple of days ago which triggered this post. My question/suggestion is about the y-axis in hist. There are reasons to prefer making the y-axis density=relative frequency/bin width. One reason is that the height of the plot does not depend on the bin width; another is that if your histogram is in density then you can easily superimpose a smooth theoretical

I want to create a 3d plot with densities. I use the function density to first create a 2d dimensional plot for specific x values, the function then creates the density and puts them into a y variable. Now I have a second set of x values and put it again into the density function and I get a second set of y variables and so on.... I want to put those sets into a 3d plot, I hope you know what I

I have spatial data in 2 dimensions - say (x,y). The correlation between x and y is fairly substantial. My goal is to use a non-parametric approach to estimate the multivariate density describing the spatial locations. Ultimately, I would like to use this estimated density to determine the area associated with a 95% probability contour for the data. Given the strong correlation between x and

Problem: Based on a n x 2 data matrix i want a kernel estimate of the bivariate density. However, i also wish to specify wich points the density should be calculated at. I can offcourse just write the full kernel density estimate as a R-code, but surely there must already exist some package for this operation? The package density(), seems to create a new matrix (depending on n), where the

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

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

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,

Hi, I have a vector or random variables and I'm estimating the density using "bkde" function in the KernSmooth package. The out put contains two vectors (x and y), and the R documentation calls y as the density estimates, but my y-values are not exact density etstimates (since these are numbers larger than 1)! what is y here? Is it possible to get the true estimated density at each

Dear R-Devel group, My name is Alexey, a data scientist from Moscow, currently working for Align Technology Inc. We have recently had a discussion of the results that the dgamma function (stats) returns for an extreme point (x == 0). <dgamma(0,1,1,log = FALSE) [1] 1 and <dgamma(0,0.5,1,log = FALSE) [1] Inf Density appears to be defined in point zero for the distribution with the

Visual inspection of the plot of a density() function vs a normal with the same mean and variance suggests the area under the density curve is bigger than under the normal curve. The two curves are very close over most of the domain. Assuming the normal curve does integrate to 1, this implies the area under density() is > 1. Is there any assurance that the density kernel smoother produces

Hello, I am a little bit confused regarding the density values obtained from the function kde2d() from the package MASS because the are not in the intervall [0,1] as I would expect them to be. Here is an example: x <- c(0.0036,0.0088,0.0042,0.0022,-0.0013,0.0007,0.0028,-0.0028,0.0019,0.0026,-0.0029,-0.0081,-0.0024,0.0090,0.0088,0.0038,0.0022,0.0068,0.0089,-0.0015,-0.0062,0.0066) y <-

Dear R users, This is a KDE beginner's question. I have this distribution: > length(cap) [1] 200 > summary(cap) Min. 1st Qu. Median Mean 3rd Qu. Max. 459.9 802.3 991.6 1066.0 1242.0 2382.0 I need to compute the sum of the values times their probability of occurence. The graph is fine, den <- density(cap, from=min(cap), to=max(cap), give.Rkern=F)

Hi I was trying to have a graph whose axes are X axis: m, Y axis: var[X ((a,b) in suffix, and (n,d) in the power)]. X ((a,b) in suffix, and (n,d) in the power)- X^(n,d) _ (a,b). Actually I require many plots involving different values of a,b,n,d, so need to keep this complicated notation. The expression() didn't work out for this case. Can anyone help me out. Thanks, in advance.

Hello, To indicate probability densities instead of counts on a histogram, I specify freq = FALSE. However, I expect that summing all top y coordinates over all the intervals of the histogram will provide 1. 1) v <- c(0.2885, 0.2988, 0.3139, 0.2615, 0.3179, 0.3163, 0.2583, 0.3052, 0.2527, 0.3147, 0.3235, 0.2408, 0.2480, 0.3108, 0.3577, 0.2829, 0.2694, 0.3275, 0.3314, 0.2639, 0.3076,

Does anyone know of a package that uses C code to calculate a multivariate normal density? My goal is to find a faster way to calculate MVN densities and avoid R loops or apply functions, such as when X and mu are N x K matrices, as opposed to vectors, and in this particular case, speed really matters. I would like to be able to use .C or .Call to pass X, mu, Sigma, and N to a C program and have

An object of class `density' has `x' and `y' components, which are what you need to do the plot. Just bind them into a matrix and use write() to write to a file. HTH, Andy -----Original Message----- From: Hinnerk Boriss [mailto:boriss at izbi.uni-leipzig.de] Sent: Thursday, November 14, 2002 7:12 AM To: R-help at stat.math.ethz.ch Subject: [R] evaluating density objects Hi! Is

I am working on creating an R package for doing fire department analysis and am trying to create a function that can display emergency incident densities. The following code sort of does the trick, but I need a display that shows the number of incidents per square mile. I believe the code below shows incidents per square unit (in this case, degrees lat/long). To solve this problem, I believe

The following code is giving me problems. I want to export densities of a distribution to a csv file. At the bottom of the code I use the hist function to generate the densities. But hist is returning values greater than 1. I don't understand, why. Any help you can supply is greatly appreciated. # Set word path dir<-"~/Research/MR Distribution Analysis/" setwd(dir)