similar to: how to estimate parameters of multimodal distribution

Dear R mailing list members, ? I am trying to find a way to assign samples into groups depending on their mathematical distribution in a computational way (that is, looped many times). My input is by row, a series of 34 samples per row with n rows. The goal would be to compute the distribution of row n and assign for that row n the samples into bins that would match the distribution of the given

Dear R community, I was trying to estimate density at point zero of a multivariate distribution (9 dimensions) and for this I was using a multinormal approximation and the function dmvnorm , gtools package. To have a sense of the error I tried to look the mismatch between a unidimensional version of my distribution and estimate density at point zero with function density, dmvnorm and dnorm. At

Dear List, I have a set of distributions recorded at an equal interval of time and I would like to plot them as series of horizontal histograms (with the x-axis representing time, and y-axis representing the bins) since the distribution shifts from unimodal to multimodal in several occasions. What I would like to see is something close to a violinplot, but I do not want a kernel density

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

I'm trying to provide different parameters to the integrate function for various probability functions. I'm using dnorm as the simplest example here. For instance integrate(dnorm, -1.96, 1.96) produces the correct answer for a normal distribution with mean 0 and standard deviation 1. I've tried two ways to use mean=2.0 and standard deviation 1, but with no luck. The examples follow.

Dear R-help, I'm using the R2WinBUGS package and getting an error message: Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file 'codaIndex.txt', reason 'No such file or directory' I'm using R 2.2.1 and WinBUGS 1.4.1 on a windows machine (XP). My R code and WinBUGS code is given below.

Dear R-users, I would like to know if there is a function (in base R or the extension packages) that would automatically detect the break points in a vector x for later use in the cut function. The idea is to determine the boundaries of the n intervals (n>=1) delimiting clusters of data points which could be considered "reasonably" close, given a numerical vector x with unknown

Howdee, One of my student spotted something I can't explain: a probability >1 vs a normal probability density function. > dnorm(x=1, mean=1, sd=0.4) [1] 0.9973557 > dnorm(x=1, mean=1, sd=0.39) [1] 1.022929 > dnorm(x=1, mean=1, sd=0.3) [1] 1.329808 > dnorm(x=1, mean=1, sd=0.1) [1] 3.989423 > dnorm(x=1, mean=1, sd=0.01) [1] 39.89423 > dnorm(x=1, mean=1, sd=0.001) [1]

I am attempting to wrap the histogram function in my own custom function, so that I can quickly generate some standard plots. A part of what I want to do is to draw a normal curve over the histogram: > x <- rnorm(1000) > hist(x, freq=F) > curve(dnorm(x), lty=3, add=T) (for normal use, x would be a vector of empirical values, but the rnorm() function works for testing) That

Hello, I am sorry, I have this problem before and Uwe send me the answer but I misplaced it and can not find it. writing a model for BRugs > library(BRugs) Loading required package: coda Loading required package: lattice Welcome to BRugs running on OpenBUGS version 3.0.3 > setwd("c:/tmp") Error in setwd("c:/tmp") : cannot change working directory > mo <-

Dear all, I want to min "integrate( (p1*dnorm+p2*dnorm+p3*dnorm)^(1.3))" for p, mu, and sigma. So, I have to estimate 8 parameters(p3=1-p1-p2). I got this warning-"Error in integrate(numint, lower = -Inf, upper = Inf) : non-finite function value." My questions are How could I fix it? I tried to divide into several intervals and sum up, but I got same message. My code is

Dear R-people, I have to compute C - -(pnorm(B)*dnorm(B)*B + dnorm(B)^2)/pnorm(B)^2 This expression seems to be converging to -1 if B approaches to -Inf (although I am unable to prove it). R has no problems until B equals around -28 or less, where both numerator and denominator go to 0 and you get NaN. A simple workaround I did was C <- ifelse(B > -25, -(pnorm(B)*dnorm(B)*B

Hi, Having a weird problem with the integrate function. I have a function which calculates a loss density: I'd like to integrate it to get the distribution. The loss density function is: lossdensity<-function(p,Beta,R=0.4){ # the second derivative of the PDF # p is the default probability of the pool at which we are evaluating the lossdensity # Beta is the correlation with the market

Dear R-users Idea: Plot a dnorm line using specific mean/sd to complete a histogram (skewed). xs:range of y-values, ys: dnorm function Problem: I expected to multiply the ys function with the sample size (n=250-300). I was wondering about a factor between 12'000 and 30'000 to match the size of the dnorm line with the specific histogram. Thanks Sibylle hist(Biotree[Ld,]$Height2008,

Hello, Can anyone give me some suggestion in term of calculating the sum below. Is there a function in R that can help doing it faster? x1, x2, ...xn where xi can be 0 or 1. I want to calculate the following: sum{ beta[a+sum(xi), b+n-sum(xi) ]* [ (1-x1)dnorm(0,1)+x1dnorm(2,1) ]* [ (1-x2)dnorm(0,1)+x2dnorm(2,1) ]* ...* [ (1-xn)dnorm(0,1)+xndnorm(2,1) ] } The sum in the beginning is over all

Full_Name: Joerg Polzehl Version: 2.3.1 OS: x86_64, linux-gnu Submission from: (NULL) (62.141.176.22) I observed an incorrect behavior of function deriv when evaluating arguments of dnorm deriv(~dnorm(z,0,s),"z") expression({ .value <- dnorm(z, 0, s) .grad <- array(0, c(length(.value), 1), list(NULL, c("z"))) .grad[, "z"] <- -(z * dnorm(z))

Hello all, I have a question about basic statistics. Given a PDF value of 0.328161, how can I find out the value of -0.625 in R? It is like reversing the dnorm function but I do not know how to do it in R. > pdf.xb <- dnorm(-0.625) > pdf.xb [1] 0.328161 > qnorm(pdf.xb) [1] -0.444997 > pnorm(pdf.xb) [1] 0.628605 Many thanks, Edwin -- View this message in context:

Hi all, Can anyone please take a look at the following two functions. The answer does not seem to be right. Thank you very much! f1 <- function(x) {integrand <- function (x, mu){ dnorm(x, mean=mu, sd=1)*dnorm(mu, mean=2, sd=1) } integrate(integrand, -Inf, Inf,x)$val } f2 <- function(x) {integrand <- function (x, mu){

I want to plot the unstadardized version of a normal plot.  Can you explain why that is not working? Dev.set(1) xcrit=-1.645 cord.x <- c(-3,seq(-3,xcrit,0.01),xcrit) cord.y <- c(0,dnorm(seq(-3,xcrit,0.01)),0)            # what does final 0 do here? curve(dnorm(x,0,1),xlim=c(-3,3),main='Normal PDF') polygon(cord.x,cord.y,col='orange')

Hi all, I have some some problem with regard to finding the integral of a function containing an indicator function. please see the code below: func1 <- function(x, mu){ (mu^2)*dnorm(x, mean = mu, sd = 1)*dgamma(x, shape=2)} m1star <- function(x){ integrate(func1, lower = 0, upper = Inf,x)$val} T <- function(x){ 0.3*dnorm(x)/(0.3*dnorm(x)+0.7*m1star(x))} func2 <-