similar to: R equivalent to Fortran, GAUSS, or Perl's "goto"

Hello All, I am a R newbie and am probably misinterpreting something really obvious... In the Rcmdr package there is a scatter3d() function that can fit a curve and also provide coefficients for the model. If I'm understanding this right, I think it's calling the lower level stats package function lm(), which is the part that actually does the curve fitting. Anyway, what has me

My site : https://radiobiscottes.studioo.fr/ -------- Norbert Deleutre  <http://www.lmgc.univ-montp2.fr/perso/norbert-deleutre/> P 0467149655 UMR CNRS 5508  <http://www.lmgc.univ-montp2.fr/> A Campus Saint-Priest/Montpellier > Le 25 nov. 2020 à 13:04, Damien GENESTE <d.geneste at illud.fr> a écrit : > > Hello Norbert, > > I don't know how i can help

I'm just curious, but wondering if there has been any work in making these algorithms available in R. They are aimed at accelerating matrix-vector products using approximation ideas, and might be useful in applications such as kernel machines, Gaussian processes/kriging. Thanks Mark Palmer Landscape Monitoring and Modelling CSIRO Mathematical and

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; @

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'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 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)

Dear Miss R, I am trying to plot a scatterplot in which the points (round) should have two colors: half red and half blue (if you want: two half solid circles put together. Can you please help me to realize this efficiently? Thank you, Best regards, Georg. *************************************** Georg Ehret Geneva University Hospital Geneva, Switzerland [[alternative HTML version deleted]]

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 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

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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

Dear All, Is it possible to import GAUSS .FMT files into R? Thanks for your time. Kind Regards, Pedro N. Rodriguez [[alternative HTML version deleted]]

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)

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

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