similar to: Incomplete Gamma Functions and GammaDistribution Doc errata.

I installed the locfit package under Linux (gcc 2.7.2). Installation was ok but > x <- runif(200) > y.compl <- 10*x*x*rgamma(200,3) > med.y <- median(y.compl) > cens <- ifelse(y.compl<=med.y,1,0) > y <- cens * y.compl + (1-cens)*med.y > library(locfit) > m <- locfit(y~x,cens=cens,family="gamma") /usr/local/src/R-0.61.1/bin/R.binary: can't

Dear R-list, I try to transform a mathematica script to R. #######relevant part of the Mathematica script (* p_sv *) dd = NN (DsD - DD^2); lownum = NN (L-DD)^2; upnum = NN (H-DD)^2; low = lownum/(2s^2); up = upnum/(2s^2); psv = NIntegrate[1/(s^NN) Exp[-dd/(2s^2)] (Gamma[1/2,0,up] + Gamma[1/2,0,low]),{s,sL,sH}, MinRecursion->3]; PSV = psv/Sqrt[2NN]; Print["------------- Results

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 all. I may be wrong, (and often am), but in trying to determine how to calculate the erf function, the documentation for 'pnorm' states: ## if you want the so-called 'error function' erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1 ## and the so-called 'complementary error function' erfc <- function(x) 2 * pnorm(x * sqrt(2), lower=FALSE) Should, instead, it read:

Hello I am trying to evaluate an Incomplete gamma function in R. Library Zipfr gives the Igamma function. From Mathematica, I have: "Gamma[a, z] is the incomplete gamma function." In[16]: Gamma[9,11.1] Out[16]: 9000.5 Trying the same in R, I get > Igamma(9,11.1) [1] 31319.5 OR > Igamma(11.1,9) [1] 1300998 I know I have to understand the theory and the math behind it rather

Hi anyone coded up Stirling numbers in R? [I need unsigned Stirling numbers of the first kind] cheers Robin -- Robin Hankin Uncertainty Analyst National Oceanography Centre, Southampton European Way, Southampton SO14 3ZH, UK tel 023-8059-7743

Hello, fellow Rdicts, I have the code for the program below. I need to integrate a function of "x" and "p". I use integrate to integrate over "x" and pass "p" as an additional argument. "p" is specified and given default value in the argument list. Still, integrate() cannot read "p", unless I explicitly insert a numeric value in the

hello my data is data2:2743 4678 21427 6194 10286 1505 12811 2161 6853 2625 14542 694 11491 14924 28640 17097 2136 5308 3477 91301 11488 3860 64114 14334 by calculating shape<-(mean(data2))^2/var(data2) scale<-var(data2)/mean(data2) i get the idea what the parameters of the gammadistribution would be. but if i try using the method mle() i get stock and i don't know, how to

Do any of the R libraries have an implementation of the Inverse Error Function (Inverse ERF)? ref: http://mathworld.wolfram.com/InverseErf.html http://functions.wolfram.com/GammaBetaErf/InverseErf/ Thanks, Nathan [[alternative HTML version deleted]]

To R-helpers, R offers the polyroot function for solving mentioned equations iteratively. However, Dr Math and Mathworld (and other places) show in detail how to solve mentioned equations non-iteratively. Do implementations for R that are non-iterative and that solve mentioned equations exists? Regards, Mads Jeppe

I'm having trouble with the standard deviation distribution as shown on http://mathworld.wolfram.com/StandardDeviationDistribution.html . (Eric Weisstein references Kenney and Keeping 1951, which I can't check.) I believe the graphs they show, but when I code the function in R, according to the listed formula, I get very different graphs. Would someone please point out my error or tell

I have 'grouped' data, that is in the form of: Interval Median 0-9.9%: -25 10-19.9%: 0 20-29.9%: 3 30-39.9%: 10 40-49.9%: 50 50-59.9%: 200 et cetera and want to fit a three parameter gamma distribution. Does anyone know of an existing routine for doing this (or something similar)? Any help or comment is much appreciated. Regards, M?rten M?rten

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

Full_Name: Tim Middelkoop Version: 0.64.0 OS: IRIX 6.3 on O2 Submission from: (NULL) (128.119.88.192) Various IRIX complile issues src/nmath/pnt.c IRIX cc does not like double negatives Makeconf,config.site,etc/Makeconf f77 pic hack, should change Makeconf.in or other... change -PIC with -KPIC enjoy, tim... === diff -ru orig/R-0.64.0/src/nmath/pnt.c R-0.64.0/src/nmath/pnt.c ---

Here's a contribution from Ian Smith that got bounced from the list. -------- Original Message -------- Subject: Re: [Rd] pt inaccurate when x is close to 0 (PR#9945) Date: Thu, 11 Oct 2007 06:02:43 -0400 From: iandjmsmith at aol.com To: murdoch at stats.uwo.ca Duncan, I tried sending the rest of this to R-devel but it was rejected as spam, hence the personal e-mail. R calculates the pt

I am looking for a method of dealing with the modified Bessel function K_\nu(x) for large \nu. The besselK function implementation of this allows for dealing with large values of x by allowing for exponential scaling, but there is no facility for dealing with large \nu. What would work for me would be an lbesselK function in the manner of lgamma which returned the log of K_\nu(x) for large

If I use the Ineq library and the Gini function in this way: >Gini(c(100,0,0,0)) I obtain the result 0.75 instead of 1 (that is the perfect inequality). I think Gini's formula in Ineq is based on a formula as reported here: http://mathworld.wolfram.com/GiniCoefficient.html but in the case of perfect inequality: x_1=.......=x_n-1 =0 x_n>0 these formula are equal to 1 - 1/n, not to

Dear R users, This is more a statistical question rather than an R question. I'd appreciate it if you can give me some suggestions. I have a sample of a time series (sample size 500, fat tail in density). I am trying to calculate the Standard error of standard deviation of a sub-block-sample (sample size 250). I take 100 this kind of sub-block-sample, randomly. For these 100 subsamples, I

Hi the following gave me a shock: > atan(2) [1] 1.107149 > atan(2+0i) [1] -0.4636476+0i > or, perhaps more of a gotcha: > atan(1.0001+0i) [1] -0.7853482+0i > atan(0.9999+0i) [1] 0.7853482+0i > evidently atan()'s branch cuts aren't where I thought they were. Where do I look for documentation on this? -- Robin Hankin Uncertainty Analyst National

Apologies for the off-topic question; as usual I'm trying to draw upon the unparalleled knowledge and sagacity of the r-help list. Please reply off-list if you can help me out. A collaborator of mine found a formula we need, on sheets which he had photocopied out of a book, some years ago. He cannot remember which book (he's getting to be as senile and forgetful as I am, poor bloke!).