search for: fm_poi

...orward to clean-up such as supplying better variable names, etc., just wanted to see if there were better overall suggestions before getting too far on this route.] Thanks in advance. cheers, Dave library(Hmisc) library(pscl) ## data data("bioChemists", package = "pscl") fm_pois <- glm(art ~ ., data = bioChemists, family = poisson) summary(fm_pois) ### pull out rate-ratios and 95% CI rr <- exp(cbind(coef(fm_pois), confint(fm_pois))) rr ### round to 2 decimal places rr <- round(rr, 2) ### plot par(mfrow=c(1,3)) plot(0, type = "n", xlim=c(0,2), ylim=c(1...

...Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome. The data and the model are based on the examples in zeroinfl(). #aproximate prediction intervals with Poisson regression fm_pois <- glm(art ~ fem, data = bioChemists, family = poisson) newdata <- na.omit(unique(bioChemists[, "fem", drop = FALSE])) prediction <- predict(fm_pois, newdata = newdata, se.fit = TRUE) ci <- data.frame(exp(prediction$fit + matrix(prediction$se.fit, ncol = 1) %*% c(-1.96, 1.96)...

Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables