Hi, this is a question about bootstrapping, it relates more to the concept than to the R package boot. But I wonder below if boot can help me. I have the below to calculate a certain point estimate:
estimate= (0.9 * 0.03 * 700000 * (((77 * (76 / 76.0)) / 83107) - ((174 * (154 / 154.0)) / 376354))) = 8.77311 Now I want to calculate confidence intervals around that estimate so I thought that since some are binomial proportions I would use random binomial estimates around those within the formula such as to have: boot_data <- (0.9 * 0.03 * 700000 * (((rbinom(10000, 83107, (77 / 83107)) * (rbinom(10000, 76, (70 / 76)) / 76.0)) / 83107) - ((rbinom(10000, 376354, (174 / 376354)) * (rbinom(10000, 154, (138 / 154)) / 154.0)) / 376354))) and then plotting the histogram and getting the quantiles to derive the 95% confidence intervals. In this case I would get: > quantile(boot_data, probs=c(0.025, 0.5, 0.975)) 2.5% 50% 97.5% 4.476521 8.258544 12.434737 Is this a good way of using the bootstrap? What other ways could I use to get a better confidence interval that is estimated using better methods than the quantile method? I now the boot package can give the CI using ABc, BC and bootstrap-t for parametric and non-parametric situations. I think since I don't have any (known) distribution on my formula to get the data values, what is the most appropriate way to use the boot? -- View this message in context: http://r.789695.n4.nabble.com/bootstrap-boot-unknown-distribution-tp3038020p3038020.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.