similar to: integration of two normal density

Hello, everyone, I have a question about integration of product of two densities. Here is the sample code; however the mean of first density is a function of another random variable, which is to be integrated. ## f=function(x) {dmvnorm(c(0.6, 0.8), mean=c(0.75, 0.75/x))*dnorm(x, mean=0.6, sd=0.15)} integrate(f, lower=-Inf, upper=Inf) ## error message Error in dmvnorm(c(0.6, 0.8), mean = c(0.75,

Hi everyone, I am confused regarding the function "density". suppose that there is a sample x of 100 data points, and plot(density(x)) gives it's pdf ? or it's more like histogram only ? thanks for any answering Carrie [[alternative HTML version deleted]]

I would like to estimate a 95% highest density area for a multivariate parameter space (In the context of anova). Unfortunately I have only experience with univariate kernel density estimation, which is remarkebly easier :) Using Gibbs, i have sampled from a posterior distirbution of an Anova model with k means (mu) and 1 common residual variance (s2). The means are independent of eachother, but

Hi I have created the following plot over the empirical returns.. What I now want to do is to overlay a curve/line with the normal density as a comparison of the two. Does anyone know how to do this? (NB the last two lines are the problem, and are wrong, I know). Thank you in advance! Rikke http://r.789695.n4.nabble.com/file/n3768783/S%26P_500_spot_and_return_2010.csv

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

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

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

Hello, I'm using dmnorm from the package {mnormt} and getting strange results. First, according to the documentation, dmnorm should return a vector of densities, and I'm only getting one value returned (which is what I would expect). I've been interpreting this as the joint density of all values in the x vector (which is what I want). Should a vector of densities be returned, and if

Dear all, I wrote the following code to calculate the density functions for two data sets, respectively. den_str <-density(str_data$Similarity); den_non_str <-density(nonstr_data$Similarity); However, I would like to knowing the difference between den_str and den_non_str, that is, the difference between the region under the curve of the den_str and the region under the curve of

Hello I would like to see the algorithm that R uses to generate density functions for several distributions (i.e. Normal,Weibull, etc). I tried: >dnorm function (x, mean = 0, sd = 1, log = FALSE) .Internal(dnorm(x, mean, sd, log)) <environment: namespace:stats> How can I see the code used for densities? Thanks!

this is probably not really a R specific question, if so apologies for off-topic posting: I'm interested in the probability density function of the maximum values from repeated samples of size N from a normal distribution: smp <- rnorm(N, meanval, stdev) with some mean 'meanval' and standard deviation 'stdev'. I would like to know what is the frequency distribution of

Is there code for bivariate kernel density estimation? For bivariate kernels there is kde2d in MASS kde2d.g in GRASS KernSur in GenKern (list probably incomplete) but none of them seems to accept a weight parameter (like density does since R 2.2.0) -- Erich Neuwirth, University of Vienna Faculty of Computer Science Computer Supported Didactics Working Group Visit our SunSITE at

r-devel list, I have recently written an R package that solves a linear least squares problem, and computes the multivariate normal density function. The bulk of the code is written in C, with interfacing code to the BLAS and Lapack libraries. The motivation here is speed. I ran into a problem computing the determinant of a symmetric matrix in packed storage. Apparently, there are no explicit

Hi folks: Is there a lookup function that returns the variate given the cumulative probability for an object returned by the density(...) function? > mydata _ as.vector(mymatrix) > mydata.density _ density(mydata) > mydata.p80 _ lookup(mydata.density, p=0.8) # is there any function to accomplish this task? Thanks. Rajiv. -------- Rajiv Prasad, Postdoctoral Research Associate,

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

Hello,   Could someone help me please and to tell how to get the probability from empirical DENSITY (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “dnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical DENSITY function (which does not correspond to any typical

Hi, I would like to know, if there's any R function, which allows calculation of probability values (0,1) from multivariate normal densities. I would be grateful for any output. Cheers, MG

Hi all, for example: > X<- rnorm(1000) > X0 <- 0.899 I want to draw a density distribution plot with a arrow pointer indicating the position of X0, meanwhile, giving out the p-value. any functions? Thanks very much.

Using R-2.12.1 and latticeExtra-0.6-14, I would like to understand why a rootogram displaying samples from the Poisson distribution looks like I expected it, whereas a rootogram using the normal distribution does not: library(latticeExtra) rootogram(~rpois(1000, lambda = 50), dfun = function(x) dpois(x, lambda = 50)) rootogram(~rnorm(1000), dfun = function(x) dnorm(x,mean(x),sd(x))) I

Hello, I am trying to compute the density of a variable k that is either (1) Normally distributed; (2) Log-Normally distributed; or (3) follows proportional error distribution. I tried to search R-help and the answer for normal distribution was easy to find (please see 1c). I am not sure if my formula for dlnorm is correct (please see 2c below)? I really don't know what function to use for the