similar to: Using Gauss with R

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

Dear people, I'm running RedHat 9.0 and R : Version 1.7.1 (2003-06-16) from the help file # Usage: # # filter(x, filter, method = c("convolution", "recursive"), # sides = 2, circular = FALSE, init) # init: for recursive filters only. Specifies the initial values of # the time series just prior to the start value, in reverse # time

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

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'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

Dear All, I've used the X-12-ARIMA or its earlier versions from S+ and R under both Unix and Windows platforms for many years using the klugey approach of calling an executable using in R the system function. I've found this serviceable for the following reasons. 1) Paul Gilbert's hunch is correct that many of the subroutines have extensive IO calls (especially the X-11 engine)

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

Dear R-users, One of the main reasons I moved from GAUSS to R (as an econometrician) was because of the existence of the library LOCFIT for local polynomial regression. While doing some checking between my former `GAUSS code' and my new `R code', I came to realize LOCFIT is not quite doing what I want. I wrote the following example script:

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)

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]]

Hello list: (This is probably a stupid question).  Is there a "quick and easy" way to confirm the gauss-markov conditions of a linear multiple regression model?  That the mean of the residuals is 0 can easily be tested for. The normality of the residuals as well (shapiro-wilk?).  But what about homoscedasticity? And independence of residuals with respect to the model variables? Thanks

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

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

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

Hello, i have a problem with the function nls(). This are my data in "k": V1 V2 [1,] 0 0.367 [2,] 85 0.296 [3,] 122 0.260 [4,] 192 0.244 [5,] 275 0.175 [6,] 421 0.140 [7,] 603 0.093 [8,] 831 0.068 [9,] 1140 0.043 With the nls()-function i want to fit following formula whereas a,b, and c are variables: y~1/(a*x^2+b*x+c) With the standardalgorithm