similar to: Distributional characteristics

Hi all, Running simulations, I'm generating market response to 2 factors X&Y.. There is no closed form for the market response.. The results are store in a matrix Z(X <- seq(.02,.98,.02), Y <- seq(.01,.19,.01)).. For optmization purpose I need to approximate the values for any factor X in 0,02-0,98 and Y in 0,01-0,19 How can I do it ? For one factor : Xn-1 < x <= Xn

Dear R-list members, I have a problem with translating a mathematica script into R. The whole script is at the end of the email (with initial values for easy reproduction) and can be pasted directly into R. The problematic part (which is included below of course) is <--- Original Mathematica ---> (* p_svbar *) UiA = Ni (Dsi - 2Di A + A^2)/2; UiiA = Nii (Dsii - 2Dii A + A^2)/2; psvbar =

Full_Name: Version: 2.7.1 (2008-06-23) OS: windows vista Submission from: (NULL) (216.82.144.137) Hello, I have been working with various probability distributions in R, and it seems the gamma distribution is inaccurate for some inputs. For example, qgamma(1e-100, 5e-101, lower.tail=FALSE) gives: 1.0. However, it seems this is incorrect; I think the correct answer should be

Hi all, I've just installed R on my Mac & PC. The base demo run fine.. But I'm encountering several pb with some packages (installed using CRAN binaries using the menu) - Lattice: > demo(lattice,package='lattice') demo(lattice) ---- ~~~~~~~ Type <Return> to start : > require(grid) [1] TRUE > old.prompt <- grid.prompt(TRUE) > old.settings

Hi all, I'm trying to estimate a nested model (purchase decision, cloglog formula, & quantity bought given a purchase, truncated Poisson). Some of the parameters are mixed (6) and 4 are fixed for all the respondent. The simulated ML (500 simulations) method forwards highly correlated estimates. After some research, Hybrid Monte Carlo seems to be a good alternative to estimate the model. I

Hi R-users, I have this code to uniformise the data using gamma: > length(dp1) [1] 696 > dim(dp1) [1] 58 12 > dim(ahall) [1]  1 12 > dim(bhall) [1]  1 12 > trans_dt <- function(dt,a,b) + { n1 <- ncol(dt) +   n2 <- length(dt) +   trans  <- vector(mode='numeric', length=n2) +   dim(trans) <- dim(dt) +   for (i in 1:n1) +   {  dt[,i] <- as.vector(dt[,i])

Hi Developers, The alpha, compiles successfully, but it is failing make check-all (on two seperate machines, both FreeBSD 6.1). Here is the version string: platform i386-unknown-freebsd6.1 arch i386 os freebsd6.1 system i386, freebsd6.1 status alpha major 2 minor 3.0 year 2006 month 03 day 27 svn rev

Hi, I want to create upper and lower 95% confidence intervals for a p-p plot of an empirical distribution with a theoretical gamma distribution. This is my code: x<-rgamma(100,shape=2, rate=1) # empirical data fitdistr(x,"gamma") # fit a gamma distribution dist<-pgamma(x,shape=1.9884256 ,rate=0.8765314 ) # fitted distribution, using the loglikelihood estimated parameters

Dear R helpers, I need to use KS and AD test for Generalized Pareto and Generalized extreme value. E.g. if I need to use KS for Weibull, I have teh syntax ks.test(x.wei,"pweibull", shape=2,scale=1) Similarly, for AD I use ad.test(x, distr.fun, ...) My problem is fir given data, I have estimated the parameters of GPD and GEV using lmom. But I am not able to find out the distribution

In the current R-devel sources, the `pgamma' function gives a warning when the `shape' argument is passed as a vector of length > 1. That is, > pgamma(3, 1) [1] 0.950213 > pgamma(3, c(1, 2)) [1] 0.9502129 0.8008517 Warning message: the condition has length > 1 and only the first element will be used in: if (shape <= 0) stop("shape must be strictly positive")

Hello all, In the course of trying to implement the CDF of an InverseGammaDistribution, I have run across the need for an igamma() function. Several others have needed this function but the answers I have found so far are not totally clear to me. I'm writing for three reasons: 1) to present a small error in the docs 2) to clarify the approach we are expected to take 3) to request,for the

Dear R experts, I'm trying to call 'pchisq' from within a C subroutine. The following error is returned: ** NON-convergence in pgamma()'s pd_lower_cf() f= nan. This error message is not printed the first time I call 'pchisq' from the C subroutine, but the second time or the next time I call 'pchisq' from within R. My session output is shown below:

Full_Name: Morten Welinder Version: 2 OS: Solaris/space/gcc2.95.2 Submission from: (NULL) (65.213.85.217) I changed src/nmath/standalone/test.c to read: --------------------------------------------------------------------------------- #define MATHLIB_STANDALONE 1 #include <Rmath.h> #include <stdio.h> int main() { double x; for (x = 99990; x <= 100009; x++) printf

Hi there, Could somebody help me disect this legacy R script I inherited at work, I have two questions: 1. I've tried to upgrade our R version from 1.6.2 (yeah, I know), to R 2.0, but some of the lines in this script are not compatible with R 2.0, could someone help me figure out where the problem is? 2. the jpeg generated (attached) seems to be off on some of the data, is there a better way

dgamma and friends in S are documented as dgamma(x, shape, rate=1) pgamma(q, shape, rate=1) qgamma(p, shape, rate=1) rgamma(n, shape, rate=1) whereas R has dgamma(x, shape, scale=1, log = FALSE) pgamma(q, shape, scale=1, lower.tail = TRUE, log.p = FALSE) qgamma(p, shape, scale=1, lower.tail = TRUE, log.p = FALSE) rgamma(n, shape, scale=1) Note the use of rate vs scale. Indeed, as both S and

I am not sure whether I'm doing something wrong or there is a bug in the documentation of ks.test. Following the posting guide, as I'm not sure, I haven't found this in the bug tracker, and the R FAQ says that stats is an Add-on package of R, I think this is the place to send it. ?ks.test provides the example <QUOTE> # Does x come from a shifted gamma distribution with shape 3

Hello. Here is my R code. I used the functional data . Now I need to use the functional data by applying the kernels instead of the xi, yi functions. Bonjour. Voici mon code en R . J'ai utiliser les donn?es fonctionnelles . Maintenant j'ai besoin d'utiliser les donn?es fonctionnelles en appliquant les noyaux ? la place des fontions xi, yi library(MASS)

Hi I need to find the Hessian matrix for a complicated function from a certain kind of data but i keep getting this error Error in f1 - f2 : non-numeric argument to binary operator the data is given by U<-runif(n) Us<-sort(U) tau1<- 2 F1tau<- pgamma((tau1/theta1),shape,1) N1<-sum(Us<F1tau) X1<- Us[1:N1]

Hi, I'm using the power.law.fit function from the igraph package to fit a power law distribution to some data. This function returns the power law exponent as it's only result. I would like to have some sort of goodness-of-fit and/or error estimate of the exponent returned. This paper: http://www.edpsciences.org/articles/epjb/pdf/2004/18/b04111.pdf suggests using the

I'm trying to figure out how to carry out a Poisson regression fit to longitudinal data with a gamma distribution with unknown shape and scale parameters. I've tried the 'lmer4' package's glmer() function, which fits the Poisson regression using: library('lme4') fit5<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) + (1|id), data=pdata,