similar to: dpois().......bizarre warning messages

I want to calculate chi square test of goodness of fit to test, Sample coming from Poisson distribution. please copy this script in R & run the script The R script is as follows ########################## start ######################################### No_of_Frouds<- c(4,1,6,9,9,10,2,4,8,2,3,0,1,2,3,1,3,4,5,4,4,4,9,5,4,3,11,8,12,3,10,0,7) N <- length(No_of_Frouds) # Estimation of

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

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 - 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

The nice new log=TRUE option in dpois appears to mess up the case where lambda=0 (I was trying to calculate the likelihood of a saturated model). Because the behavior is now always to calculate the probability in terms of exp(log(prob)), there's a test for lambda<=0 which really needs to be only lambda<0. dpois(0:5,0) ought to give 1 0 0 0 0 but gives NaNs instead. Here's

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 >

I am using the MASS library function fitdistr(x, dpois, list(lambda=2)) but I get Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : Function cannot be evaluated at initial parameters In addition: There were 50 or more warnings (use warnings() to see the first 50) and all the first 50 warnings say 1: non-integer x = 1.452222 etc Can anyone tell me what I am doing

I am trying to run the following code in R on a Linux cluster. I would like to use the full processing power (specifying cores/nodes/memory). The code essentially runs predictions based on a GAM regression and saves the results as a CSV file for multiple sets of data (here I only show two). Is it possible to run this code using HPC packages such as Rmpi/snow/doParallel? Thank you!

Hi All, My dependent variable is a ratio that takes a value of 0 (zero) for 95% of the observations and positive non-integer values for the other 5%. What model would be appropriate? I'm thinking of fitting a GLM with a Poisson ~. Now, becuase it takes non-integer values, using the glm function with Poisson family issues warning messages. Warning messages: 1: In dpois(y, mu, log = TRUE) :

A question about the inner workings of glm() and dpois(): Suppose I call glm(y ~ x, family=poisson, weights = w) where y contains NON-INTEGER (but still nonnegative) values. (a) Does glm() still correctly maximise the weighted Poisson loglikelihood ? (i.e. the function given by the same formal expression as the weighted loglikelihood of independent Poisson variables Y_i except that the

Full_Name: Murray H Smith Version: 0.63.3 OS: Windows NT Submission from: (NULL) (130.216.5.57) The ppois function is displaced by -0.5. Try: > ppois(-0.5,1) [1] 0.3678794 > ppois(-0.51,1) [1] 0 > ppois(0,1) [1] 0.3678794 and > par(mfrow=c(2,1)) > x<-seq(-1,5,0.01) > plot(x,ppois(x,1),type="s",ylab="F(x)",main="Poisson CDF?") >

We obtained some disturbing results from convolve() (inaccuracies and negative probabilities). We'll try to make the context clear in as few lines as possible... Our function panjer() (code below) basically computes recursively the probability mass function of a compound Poisson distribution. When the Poisson parameter lambda is very large, the starting value of the recursive scheme ---

Given a vector;> ab = seq(0.5,1, by=0.1)> ab[1] 0.5 0.6 0.7 0.8 0.9 1.0 The euclidean distance between the vector elements is given by the lower triangular matrix > dd1 = dist(ab,"euclidean")> dd1    1   2   3   4   52 0.1                3 0.2 0.1            4 0.3 0.2 0.1        5 0.4 0.3 0.2 0.1    6 0.5 0.4 0.3 0.2 0.1 Convert the lower triangular matrix to a full

Given a vector;> ab = seq(0.5,1, by=0.1)> ab[1] 0.5 0.6 0.7 0.8 0.9 1.0 The euclidean distance between the vector elements is given by the lower triangular matrix > dd1 = dist(ab,"euclidean")> dd1    1   2   3   4   52 0.1                3 0.2 0.1            4 0.3 0.2 0.1        5 0.4 0.3 0.2 0.1    6 0.5 0.4 0.3 0.2 0.1 Convert the lower triangular matrix to a full

Has anyone had experience with MIT Kerberos tickets not valid after server reboot? After server reboot I have to do a 'kinit' to get a new ticket, re-join the AD domain, and restart samba. Then all is fine until I have to reboot the server again.. Same thing again and again. My time is synced, Kerberos tickets are good for 500d.

The background: I'm trying to fit a Poisson-lognormal distrbutuion to some data. This is a way of modelling species abundances: N ~ Pois(lam) log(lam) ~ N(mu, sigma2) The number of individuals are Poisson distributed with an abundance drawn from a log-normal distrbution. To fit this to data, I need to integrate out lam. In principle, I can do it this way: PLN1 <- function(lam, Count,

Hi -I'm running a GAM with 7 explanatory variables with a Poisson error structure. All of the variables are continuous so I'm getting error messages in R. cod.fall.full.gam.model<-gam(Kept.CPUE~s(HOUR)+s(LAT_dec)+s(LONG_dec)+s(meantemp_C)+s(meandepth_fa)+s(change_depth)+s(seds), data=cod.fall.version2,family=poisson) In dpois(y, mu, log = TRUE) ... : non-integer x = 5.325517

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'

I have the following setup: DS3 -> Cisco AS5400 -> H.323 (ooh323) -> Asterisk Inbound calls work great but outbound calls fail. So to check and make sure we have outbound calling ability on the DS3 we setup a Cisco Call Manager Express and it can make outbound calls both local and long distance with no problems. The failure code is Cause i = 0x8381 - Unallocated/unassigned number. We

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 ~ turns out I don't need to look at the C code. ~ if one uses the mu/size parameterization of the negative binomial, R computes size/(size+mu) to switch parameterizations. If size>>mu this gets rounded to 1 ... should be easy enough to test and return NA under these circumstances? function (x, size, prob, mu, log = FALSE) { ~ if