similar to: Import GAUSS .FMT files

Dear All, Is it possible to simulate an AR(1) process via a distribution? I have simulated an AR(1) process the usual way (that is, using a model specification and using the random deviates in the error), and used the generated time series to estimate 3- and 4-parameter distributions (for instance, GLD). However, the random deviates generated from these distributions do not follow the

Hi everyone, In the package POT, there is a function that computes the L-moments of a given sample (samlmu). However, to compute those L-moments, one needs to obtain the total number of combinations between two numbers, which, by the way, requires the use of a factorial. See, for example, Hosking (1990 , p. 113). How does the function "samlmu" in the package POT avoids overflows? I

First step in allow different formats for indexes of images, aside the current one. The only accepted value is "native". --- builder/builder.ml | 5 ++++- builder/sources.ml | 16 ++++++++++++++++ builder/sources.mli | 3 +++ builder/virt-builder.pod | 16 ++++++++++++++++ 4 files changed, 39 insertions(+), 1 deletion(-) diff --git a/builder/builder.ml

Spencer Thanks for your thoughts on this. I did a bit of work and did end up with a method (more a trick), but it did work. I am certain there are better ways to do this, but here is how I resolved the issue. The integral I need to evaluate is \begin{equation} \frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta} {\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta} \end{equation} Where

Dear All, I am a long time S user and now a convert to R. As part of my general work in time series I occasionally assist groups of econometricians and others in the finance fraternity. In particular, that community has invested a large amount of time and effort in writing specialised code in Gauss. I am unfamiliar with Gauss (although I have used Matlab which is, I understand, a comparable

I believe this may be more related to analysis than it is to R, per se. Suppose I have the following function that I wish to integrate: ff <- function(x) pnorm((x - m)/sigma) * dnorm(x, observed, sigma) Then, given the parameters: mu <- 300 sigma <- 50 m <- 250 target <- 200 sigma_i <- 50 I can use the function integrate as: > integrate(ff, lower= -Inf, upper=target)

I'll be even more tangent. Those interested in Ox, see http://www.de.ufpe.br/~cribari/ox.pdf Cheers, Francisco. Date: Fri, 6 Apr 2001 09:34:19 +0100 (BST) From: Bill Simpson <wsi at gcal.ac.uk> Subject: Re: [R] Using Gauss with R This is a tangent to your question. The economist Jurgen Doornik has written a language called Ox: http://www.nuff.ox.ac.uk/Users/Doornik/doc/ox/ox.htm

Hi, I would like to solve a double integral of the form \int_0^1 \int_0^1 x*y dx dy using Gauss Quadrature. I know that I can use R's integrate function to calculate it: integrate(function(y) { sapply(y, function(y) { integrate(function(x) x*y, 0, 1)$value }) }, 0, 1) but I would like to use Gauss Quadrature to do it. I have written the following code (using R's statmod package)

The search at www.r-project.org mentioned a function "gauss.hermite{rmutil}". However, 'install.packages("rmutil")' produced, 'No package "rmutil" on CRAN.' How can I find the current status of "gauss.hermite" and "rmutil"? Thanks, Spencer Graves

Hi, I am translating some Gauss code to R. Gauss has an interesting way of handling constraints. Observe the following code snipplet: e1 = x[.,23] .eq 0; @ remove obs with Regular Hours = 0 @ e2 = x[.,12] .gt 1; @ remove obs with non-regular work status @ e3 = x[.,4] .lt 15; @ remove obs with agricultural and mining industry code (< 15)@ esum = e1 + e2 + e3; e = esum .gt 0; @

Hi, I am trying to find a function that returns simply the weights and points of an n point gauss hermite integeration, so that I can use them to fit a non-standard likelihood. I have found some documentation for the function 'gauss.hermite' written by jim lindley, but can't find the actual binary on CRAN I'm aware there are lots of functions like glmm, glmmML etc to fit mixed

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Does anyone see why my code does not integrate to 1? library(statmod) mu <- 0 s <- 1 Q <- 5 qq <- gauss.quad(Q, kind='hermite') sum((1/(s*sqrt(2*pi))) * exp(-((qq$nodes-mu)^2/(2*s^2))) * qq$weights) ### This does what's it is supposed to myNorm <- function(theta) (1/(s*sqrt(2*pi))) * exp(-((theta-mu)^2/(2*s^2))) integrate(myNorm, -Inf, Inf)

I've written a series of functions that evaluates an integral from -inf to a or b to +inf using equally spaced quadrature points along a normal distribution from -10 to +10 moving in increments of .01. These functions are working and give very good approximations, but I think they are computationally wasteful as I am evaluating the function at *many* points. Instead, I would prefer to use

On Fri, Jun 23, 2017 at 04:12:36PM +0300, Pavel Butsykin wrote: > Signed-off-by: Pavel Butsykin <pbutsykin@virtuozzo.com> > --- > m4/guestfs_appliance.m4 | 11 +++++++++++ > 1 file changed, 11 insertions(+) > > diff --git a/m4/guestfs_appliance.m4 b/m4/guestfs_appliance.m4 > index 81c43879f..7e8d28271 100644 > --- a/m4/guestfs_appliance.m4 > +++

Dear All, I need to convert some Northing-Easting coordinates from the Gauss-Krüger system into latitude-longitude. Any suggestions on how to do it? Regards, Marco Marco Giannitrapani Statistical Consultant Tel: +44151373 5945 Email: Internet: http://www.shell.com [[alternative HTML version deleted]]

Hi, It seems to me that the most suitable method in R for curve-fitting is the use of nls, which uses a Gauss-Newton (GN) algorithm, while the use of the Levenberg-Marquardt (LM) algorithm does not seem to be very stressed in R. According to this [1] by Ripley, 'Levenberg-Marquardt is hardly competitive these days' which could imply the low emphasize on LM in R. The position of LM is, to

Hi all, I am using the 'gaussHermite' function from the 'pracma' library ############ CODES ########### library(pracma) cc=gaussHermite(10) cc$x^2 cc$x^5 cc$x^4 ############ CODES ########### as far so good. However, it does NOT work for any NON integer values, say ############ CODES ########### cc$x^(2.5) cc$x^(-2.5) ############ CODES ########### But just think about it

Hello, I am having a hard time fitting a gauss beam using R. In gnutplot I did something like $ w(z) = w0 * sqrt(1+(z/z0)**2) $ fit w(z) 'before_eom.txt' using 1:2 via w0, z0 to obtain w0 and z0. Now I want to do the same in R. I tried a linear model like this (r = radius, z = distance): beam <- function(z) { sum(sqrt(1 + z**2)) } lm(r ~ I(beam(z)), data = before_eom) Which

Dear r-Help readers, 1) Is there support for NSUR in some R package yet? 2) Is there a general function of applying the Gauss-Newton or Marquard method, in which the function of calculating the partial derivatives can be specified by the user? Contemporaneously correlated non-linear models (NSUR) is a method to fit a system of non-linear equations. I want to use to fit several non-linear