similar to: lambda==0 in dpois() (PR#459)

Yes, that looks like a bug and an easily fixable one too. However, I spy another issue: Why do we check the !R_FINITE(x) && mu == x before checking for sd < 0 ? The difference is whether we return ML_NAN; or ML_ERR_return_NAN; but surely negative sd should always be an error? I'd be inclined to do if (sigma < 0) ML_ERR_return_NAN; if(!R_FINITE(sigma)) return R_D__0;

>>>>> peter dalgaard >>>>> on Sun, 8 Dec 2019 12:11:50 +0100 writes: > Yes, that looks like a bug and an easily fixable one too. agreed. > However, I spy another issue: Why do we check the > !R_FINITE(x) && mu == x before checking for sd < 0 ? The > difference is whether we > return ML_NAN; or ML_ERR_return_NAN;

>>>>> "MM" == Martin Maechler <maechler@stat.math.ethz.ch> writes: >>>>> "TL" == Thomas Lumley <thomas@biostat.washington.edu> writes: TL> On Thu, 24 Aug 2000, Troels Ring wrote: >>> Dear friends. >>> >>> Is this as expected ? Is alpha and beta too large simply ? >>>

>>>>> "MM" == Martin Maechler <maechler@stat.math.ethz.ch> writes: >>>>> "TL" == Thomas Lumley <thomas@biostat.washington.edu> writes: TL> On Thu, 24 Aug 2000, Troels Ring wrote: >>> Dear friends. >>> >>> Is this as expected ? Is alpha and beta too large simply ? >>>

Good question, I cannot speak for R's developers but I would like to provide some information on the problem. Here are the first few lines of the dnorm function located at src\nmath\dnorm.c: ``` double dnorm4(double x, double mu, double sigma, int give_log) { #ifdef IEEE_754 if (ISNAN(x) || ISNAN(mu) || ISNAN(sigma)) return x + mu + sigma; #endif if(!R_FINITE(sigma)) return R_D__0;

Hi, I've written some C code for density evaluation of the non-central t distribution. It works with the R-1.7.1 source code if placed in the src/nmath directory and after appropriate changes are made to Makefiles, to the dt function in src/library/base/R/distn.R etc. I haven't read a lot of R source code so it may need some R-ification, but I've tried to use the dt.c file as a

Hello all, I am trying to get a better understanding of the underlying code for the stats::dpois function in R and, specifically, what happens under the hood when it is called. I finally managed to track down the C course at: https://github.com/wch/r-source/blob/trunk/src/nmath/dpois.c. It would seem that the dpois C function is taking a double for each of the x and lambda arguments so I am a bit

Hi. Here is a piece of cpp code. It works, but I do not understand the rational for the use of "R::dpois" to call the function dpois since in the examples I have always found directly "dpois" or "Rcpp::dpois" that both do not work in my code. Could anyone be so patient to explain me why should it be like that? Thaks a lot, Enrico #include <Rcpp.h> using

Howdy! Has anybody successfully installed R under OpenBSD? I just tried to install the latest R-release under OpenBSD 2.7 and got the following errors in the make step: pnorm.c:62 'ML_ERR_return_NAN' undeclared pnorm.c:62 'ML_NAN' undeclared pnorm.c:62 'ML_NEGINF' undeclared --Ragnar -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

Howdy! Has anybody successfully installed R under OpenBSD? I just tried to install the latest R-release under OpenBSD 2.7 and got the following errors in the make step: pnorm.c:62 'ML_ERR_return_NAN' undeclared pnorm.c:62 'ML_NAN' undeclared pnorm.c:62 'ML_NEGINF' undeclared --Ragnar -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

Hi - I have a likelihood function that involves sums of two possions: L = a*dpois(Xi,theta1)*dpois(Yi,theta2)+b*(1-c)*a*dpois(Xi,theta1+theta3)*dpois(Yi,theta2) where a,b,c,theta1,theta2,theta3 are parameters to be estimated. (Xi,Yi) are observations. However, Xi and Yi are usually big (> 20000). This causes dpois to returns 0 depending on values of theta1, theta2 and theta3. My first

Hi, Apropos of a recent Inf question, I've previously wondered if dnorm "does the right thing" with dnorm(0, 0, -Inf) which gives zero. Should that be zero or NaN (or NA)? The help says "'sd < 0' is an error and returns 'NaN'" and since -Inf < 0 is TRUE, then... is this a bug? Thank you, Stephen Rochester, MN USA

Chuck and Greg, Thanks a lot for your help! I have a much better understanding now of what is happening ?under the hood?. Kind Regards, Jason > On 31 May 2018, at 20:08, Greg Minshall <minshall at acm.org> wrote: > > Jason, > > as Chuck Berry (to whom, *thanks* for 'do {...} while(0)'!) suggested, > using grep, or even grep executed from find, such as >

Dear Masters, I have a question to submit consider the following script m<-4.95 obs<-rpois(36,m) # i generate 36 realization from a poisson(m) hist(obs,freq=F) curve(dpois(x,m),add=T,col="red") #i wish to overlay on the histogram the theorical poisson density function errors are returned saing the x vector doesn't contain integers.... really bizarre i can't give

Hello guys, I would like to ask for help to understand what is going on in "func2". My plan is to generalize "func1", so that are expected same results in "func2" as in "func1". Executing "func1" returns... 0.25 with absolute error < 8.4e-05 But for "func2" I get... Error in dpois(1, 0.1, 23.3065168689948, 0.000429064542600244,

Hi, I have a problem with Kolmogorov-Smirnov test fit. I try fit distribution to my data. Actualy I create two test: - # First Kolmogorov-Smirnov Tests fit - # Second Kolmogorov-Smirnov Tests fit see below. This two test return difrent result and i don't know which is properly. Which result is properly? The first test return lower D = 0.0234 and lower p-value = 0.00304. The lower 'D'

Hello: I wish to again express my appreciation to all who have contributed to making R what it is today. At this moment, I'm particularly grateful for whoever modified the 'mle' code so data no longer need be passed via global variables. I remember struggling with this a couple of years ago, and I only today discovered that it is no longer the case. I'd

Hi there, I'm pretty new to the field of fitting (anything). I try to fit a distribution with mle, because my real data seems to follow a zero-inflated poisson distribution. So far, I tried a simple example to see whether I understand how to do it or not: # example count data x <- 0:10 y <- dpois(x, lambda = 1.4) # zero-inflated poisson zip <- function(x, lambda, prop) { (1 -

Full_Name: Teru KAMOGASHRIA Version: 2.0.1 OS: SunOS sun 5.10 Generic i86pc i386 i86pc Submission from: (NULL) (219.5.176.24) rbinom.c cannot be compiled because of the casting problem. /opt/SUNWspro/bin/cc -I. -I../../src/include -I../../src/include -I/usr/local/include -DHAVE_CONFIG_H -D__NO_MATH_INLINES -g -c rbinom.c -o rbinom.o "rbinom.c", line 60: operand must have real

Dear people, I'm trying to do some analysis of a data using the models by Royle & Donazio in their fantastic book, particular the following function: http://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/panel4pt1.fn that applied to my data and in the console is as follows: > `desman.y` <- structure(c(3L,4L,3L,2L,1L), .Names = c("1", "2", "3",