To demonstrate the functionality of dsrTest
the various methods to
construct confidence interval are demonstrated on the example data drawn from Table 14.4 in Fleiss (1981) (also used in Fay and Feuer (1997)).
The example data come from a study of Down Syndrome and maternal age (Stark and Mantell, 1966).
library(dsrTest) # the data are in `downs.mi` data("downs.mi", package = "dsrTest") # Birth order 5 + b5 <- downs.mi[downs.mi$BirthOrder == 5, ] # Gamma Method with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "gamma")) # Gamma Mid-p with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "gamma", control = list(midp = TRUE))) # Dobson (exact) with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "dobson")) # Dobson (Mid-p) with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "dobson", control = list(midp = TRUE))) # Asymptotic (no transformation) with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "asymptotic")) # Asymptotic (log transformation) with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "asymptotic", control = list(trans = "log"))) # Beta Method with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "beta")) # Approximate Bootstrap Method with(b5, dsrTest(Cases, Births, Standard, mult = 1e5, method = "bootstrap"))
# A list of methods to implement methods_list <- list( gamma = list( list(wmtype = "max"), list(midp = TRUE), list(wmtype = "tcz"), list(wmtype = "mean"), list(wmtype = "minmaxavg")), asymptotic = list( list(trans = "none"), list(trans = "log"), list(trans = "loglog"), list(trans = "logit")), dobson = list( list(midp = FALSE), list(midp = TRUE)), beta = list( list(wmtype = "none"), list(wmtype = "tcz"), list(wmtype = "mean"), list(wmtype = "minmaxavg"), list(wmtype = "max")), bootstrap = list(list()) ) # split out to allow call to mapply methods <-rep(names(methods_list), times = lengths(methods_list)) controls <- do.call(c, unname(methods_list)) all_methods <- mapply(dsrTest, method = methods, control = controls, MoreArgs = list(mult = 1e5, x = b5$Cases, n = b5$Births, w = b5$Standard), SIMPLIFY = FALSE) # create some "short" names control_types <- unlist(controls) control_names <- c(gsub("midp=FALSE", "Exact CI", gsub("=TRUE", "", sprintf("[%s=%s]", names(control_types), control_types))), "") names(all_methods) <- paste(methods, control_names) # combine CI into one data.frame results <- do.call(rbind,lapply(all_methods, function(data) data.frame( estimate = data$estimate, lci = data$conf.int[1], uci = data$conf.int[2]))) # and display knitr::kable(results, digits = 3)
Fleiss, JL (1981) Statistical Methods for Rates and Proportions, Wiley, New York.
Stark CR and Mantel N (1966) 'Effects of maternal age and birth order on the risk of mongolism and leukemia' J Natl Cancer Inst 37 (5) 687--698. https://doi.org/10.1093/jnci/37.5.687
Fay MP & Feuer EJ (1997). 'Confidence intervals for directly standardized rates: a method based on the gamma distribution. Statistics in Medicine. 16: 791--801. https://doi.org/10.1002/(SICI)1097-0258(19970415)16:7<791::AID-SIM500>3.0.CO;2-%23
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