similar to: simulation from truncated poisson

Dear all, I am trying to simulate from truncated Pareto distribution. I know there is a package called PtProcess for Pareto distribution...but it is not for truncated one. Can anyone please help me with this? Thanks in advance. Cassie [[alternative HTML version deleted]]

Dear All I would like to know whether it is possible with R to generate random numbers from zero-truncated Poisson distribution. Thanks in advance, Galina

I am using a binomial mixture model to estimate abundance (N) and detection probability (p) using simulated count data: -Each site has a simulated abundance that follow a Poisson distribution with lambda = 5 -There are 200 simulated sampled sites -3 repeated counts at each site - only 50 percent of the animals are counted during each count (i.e, detection probability p =0.5, see codes) We removed

Dear all, I am trying to simulate from discrete uniform distribution. But I could not find any in-built code in R. Could anyone help me please? Thanks in advance for the time and help. Cassie [[alternative HTML version deleted]]

Hi I'm a postgrad in ecology, and have recently started to use R. I'm planning to model various sets of animal abundance (i.e. count) data in relation to habitat data using glm's and/or glmmPQL's. However, some of my potential response variables have many zeros. From what I gather the "family = ..." option in the command line does not allow for the direct

Dear R users I would like to simulate underdispersed Poisson and binomial distributions somehow. I know you can do this for overdispersed counterparts - using rnbinom() for Poisson and rbetabinom() for binomial. Could anyone share functions to do this? Or please share some tips for modifying existing functions to achieve this. Thank you very much for your help and time Shinichi

Dear friends, does anyone know how I can run a zero truncated poisson regression using R (or even SPSS)?   Dr. Iasonas Lamprianou Department of Social and Political Sciences University of Cyprus [[alternative HTML version deleted]]

Dear all, I created a n*2 matrix and I want to replace the ith row with a vector. Can anyone suggest me what is the simple way to do it? Sorry for bothering you with such simple question. I appreciate any hints or advice. Thanks in advance. Cassie [[alternative HTML version deleted]]

Hi. I would like to know if is it possible to estimate zero-truncated count models with robust standard errors in R. In Stata that is possible. I already made some searches and attempts but not obtained it. In R I made the estimation of the truncated poisson by the vglm command of VGAM package . -- View this message in context:

R-users, Can an offset term be included in a Poisson model? I get an error message when trying that: >r3o <- glm(tax ~ areal + offset(o), family=poisson) Error in (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, : inner loop 1; can't correct step size In addition: Warning message: Step size truncated due to divergence in: (if (is.empty.model(mt))

Dear Listers, I have a general statistical question. Are hurdle logit-poisson model and posson model nested? Thank you so much?

Dear all, I have a very basic question: how does the logLik function work for poisson models? Example: I simulate 20 observations from a Poisson distribution with mean 800. y <- rpois(20,800) model <- glm(y ~ 1, family=poisson()) logLik(model) I would like to know what's the exact formula the function logLik uses. I looked at ?extractAIC but I cannot sort it out. Can you please

Hi there, I am trying to write a function to perform GOF test of data to a zero-truncated Poisson distribution. I am facing 2 problems. 1) How can I obtain a frequency table for all values within the range of observed values? For instance if the observations are obs <- c("A", "A", "A", "A", "B", "C", "C", "D",

Dear R-users, I'm trying to do some spatial simulation. I have two covariates, Z and C. I want to examine their relationship under different spatial distribution. I have no problem simulating completely spatial random process but I'm totally stuck on poisson (cluster) pattern. I already have a dataset with Z and C (obs=575) and I know the relationship between them. Using these 575

Dear All, I have been trying to simulate data from a fitted glm using the simulate() function (version details at the bottom). This works for lm() fits and even for lmer() fits (in lme4). However, for glm() fits its output does not make sense to me -- am I missing something or is this a bug? Consider the following count data, modelled as gaussian, poisson and binomial responses: counts

Hello everyone, I am wondering if there is any routine in R which can convert time given in 'seconds' unit to the 12 hour time format. For example, suppose the data set looks like x=c(36885,84000,20) #x in seconds I want to get the output as [1] " 11:14:45 AM" [2] " 11:20:00 PM" [3] "12:20:00 AM" Does anyone have any idea? Thanks in advance. Cassie

Hi everyone I am stuck on specifying my own maximum likelihood function for a special poisson model. My poisson model is as follow: O ~ Pois(b*N + b*RR*E) With O = observed cases b = constant (known) N = number of unexposed persons (known) E = number exposed persons (known) RR = relative risk (value is assumed under a scenario, e.g. RR=2.0) I used rpois to simulate the values of O for several

Hello, All: ????? The default "simulate" method for lm and glm seems to ignore the sampling variance of the parameter estimates;? see the trivial lm and glm examples below.? Both these examples estimate a mean with formula = x~1.? In both cases, the variance of the estimated mean is 1. ??? ??????? * In the lm example with x0 = c(-1, 1), var(x0) = 2, and

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]