similar to: Very simple loop

Dear all, Even though one of R users answered my question, I cannot understand, so I re-ask this question. I am trying to use "do.call", but I don't think I totally understand this function. Here is an simple example. -------------------------------------------- > B <- matrix(c(.5,.1,.2,.3),2,2) > B [,1] [,2] [1,] 0.5 0.2 [2,] 0.1 0.3 > x <- c(.1,.2) >

Dear all, I am trying to use "do.call", but I don't think I totally understand this function. Here is an simple example. -------------------------------------------- > B <- matrix(c(.5,.1,.2,.3),2,2) > B [,1] [,2] [1,] 0.5 0.2 [2,] 0.1 0.3 > x <- c(.1,.2) > X <- cbind(1,x) > X x [1,] 1 0.1 [2,] 1 0.2 > > lt <-

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

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

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

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. 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

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 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) :

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",

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

Why I am getting Error in integrate(f, x1, x1 + dx) : maximum number of subdivisions reached and can I avoid this? func <- function(y, a, rate, sad){ f3 <- function(z){ f1 <- function(y,a,n){ dpois(y,a*n) } f2 <- function(n,rate){ dexp(n,rate) } f <- function(n){ f1(y,a,n)*f2(n,rate) } r <- 0 r1 <- 1 x1 <- 0 dx <- 20 while(r1 >

All, I have some data on parasites on apple leaves and want to do a goodness of fit test to a Poisson distribution. This seems to do it: mites <- c(rep(0,70), rep(1,38), rep(2,17), rep(3,10), rep(4,9), rep(5,3), rep(6,2), rep(7,1)) tab <- table(mites) NSU <- length(mites) N <-

Hi all, Why I am getting, Error: evaluation nested too deeply: infinite recursion / options(expressions=)? Thanks in advance! func <- Vectorize(function(x, a, sad, trunc=0, ...) { result <- function(x) { f1 <- function(n) { dcom <- paste("d", deparse(substitute(sad)), sep="") dots <- c(as.name("n"), list(...)) f <-

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 about graphics. I would like to plot two graphs: a barplot and curve. Here is the code: > barplot(dpois(0:45,20),xlim=c(0,45),names=0:45) > curve(dnorm(x,20,sqrt(20)),from=0,to=45,add=T) Both graphs are drawn in the same figure, however the scale in both graphs dooes not match. For some reason the second plot is shifted to left. I think there is a problem

Hi, I'm trying to make multiple poisson regressions with the MLE2 command. I have used the following expression, but I receive an error message: poisfit <- mle2(y ~ dpois(exp(b0 + b1*x1 + b2*x2)), start=list(b0=1, b1=1, b2=1), data=data1) Error in optim(par = c(1, 1, 1), fn = function (p) : non-finite initial value 'vmmin' I have changed initial values using coefficient values

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