search for: poission

The expected number of bladder cancer over next 20 years a tire industry is 1.8. Poission distribution is assumed to hold and 6 reported deaths are caused by bladder cancer among the employees. Trying to find how unusual this event is. > ppois(q=6, lambda=1.8, lower.tail = TRUE, log.p = FALSE) [1] 0.9974306 not sure if ppois is the right one to use and the parameters... thx much

...ce <- var(aggregate$x) occupancymean.data.frame <- rbind(occupancymean.data.frame, data.frame(Area, Totalgrids, Occupiedgrids, Occupancy, Mean, Variance)) } occupancymean.data.frame Occupancy <- occupancymean.data.frame$Occupancy Mean <- occupancymean.data.frame$Mean poission <- nls(Occupancy ~ 1-exp(-rho*Mean), start = list(rho = 2.1), data = occupancymean.data.frame) nachman <- nls(Occupancy ~ 1-exp(-alpha*Mean^beta), start = list(alpha = 0.2, beta = 0.1), data = occupancymean.data.frame) logistic <- nls(Occupancy ~ (alpha*Mean^beta)/(1+alpha*Mean^beta)...

Hi Rers, I have a poission time series model with 5 parameters. I just wanted to remove two of the lag on response in the model and put it as a system model. I am not sure about the codes to combine these two on R. If anybody has any R example (code), please post it. My original model: log(Y(t))~constant+b1*Y(t-1)+b2...

Hi, I have a dataset. First of all, I know that my dataset shall follow the Poission distribution. Now I have two questions: 1) How can I check that my data follow the Poisson distribution? 2) How can I calculate Lambda of my data? Regards Saeed -- View this message in context: http://www.nabble.com/Finding-Lambda-in-Poisson-distribution-tp22288885p22288885.html Sent from the R h...

...e investigated (random factor pop), which belong to different habitat types (factor ht) - Within each plant population, individuals were grouped into 3 size classes (factor sz) - For each individual, some count data were recorded The independent variables I'd like to analyse are either poission of binomially distributed. For gaussian data, I would use the following model: ht + pop %in% ht + sz + sz:ht + sz : pop %in %ht ht would basically be tested against pop (because the population is the unit of replication for ht), and sz against sz:pop:ht. (the hypotheses to test are that ht...

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]

...6 0 33.71 30.62 ... I am interested in comparing fit of different specification of Generalized Linear Models (although there are some issues with using AIC or BIC for comparison, but this is the question that I like to post here). Here are two of the several models that I am interested in: (1) Poission log-linear model > pois<-glm(cnt~herbc+herbht,family=poisson,data=sotr) > summary(pois) Call: glm(formula = cnt ~ herbc + herbht, family = poisson, data = sotr) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.341254 0.089969 -14.908 <2e-16 ***...

...ot;, ylim=c(-1, 1)) lines(x.2, f.1-1.96*sqrt(diag.y), lty=4, col="Blue") lines(x.2, f.1+1.96*sqrt(diag.y), lty=4, col="Blue") However, the CI obtained this way is different from gam's output. Can anyone show us how to get the correct CI? Especially when we have a GLM, e.g., Poission model? Thanks a lot for your help. Jinsong Chen Division of Biostatistics and Epidemiology Dept. of Public Health Sciences School of Medicine University of Virginia jc8ww@virginia.edu [[alternative HTML version deleted]]

...4 classes and methods the implementation so far covers the optimally robust estimation for all(!) smoothly (L2-differentiable/differentiable in quadratic mean) parametric models which are based on a univariate distribution. Many well-known parametric (in particular, exponential) families (Binomial, Poission, Normal, Gamma, Gumbel, ...) are L2-differentiable. We include several +neighborhood types (convex contamination, total variation) +risks (MSE, Hampel, overshoot/undershoot), +bias-types (symmetric, one-sided, asymmetric) +norms (unstandardized, self-standardized, information-standardiz...

...4 classes and methods the implementation so far covers the optimally robust estimation for all(!) smoothly (L2-differentiable/differentiable in quadratic mean) parametric models which are based on a univariate distribution. Many well-known parametric (in particular, exponential) families (Binomial, Poission, Normal, Gamma, Gumbel, ...) are L2-differentiable. We include several +neighborhood types (convex contamination, total variation) +risks (MSE, Hampel, overshoot/undershoot), +bias-types (symmetric, one-sided, asymmetric) +norms (unstandardized, self-standardized, information-standardiz...

I am working on a data set which has the waiting times taken of jobs running on a cluster. I need to come up with a method to use this historical data to come up with a prediction for the future. Even probably try simulating the full history (as in I have history of the job submission time and running time,etc). So I can run through the actual history and at every job submission, depending on the

Hello, Anyone who knows a fast and accurate algorithm for generating draws from an underdispersed Poisson distribution. Or even better, if there is a package containing such an implementation. Thanks Rene