Tidy Simultaneous Confidence Intervals for Multinomial Proportions
Methods for obtaining simultaneous confidence intervals for multinomial proportions have been proposed by many authors and the present study include a variety of widely applicable procedures. Seven classical methods (Wilson, Quesenberry and Hurst, Goodman, Wald with and without continuity correction, Fitzpatrick and Scott, Sison and Glaz) and Bayesian Dirichlet models are included in the package. The advantage of MCMC pack has been exploited to derive the Dirichlet posterior directly and this also helps in handling the Dirichlet prior parameters. This package is prepared to have equal and unequal values for the Dirichlet prior distribution that will provide better scope for data analysis and associated sensitivity analysis.
The following functions are implemented:
scimp_bmde
: Bayesian Multinomial Dirichlet Model (Equal Prior)scimp_bmdu
: Bayesian Multinomial Dirichlet Model (Unequal Prior)scimp_fs
: Fitzpatrick & Scott Confidence Intervalscimp_goodman
: Goodman Confidence Intervalscimp_qh
: Quesenberry & Hurst Confidence Intervalscimp_sg
: Sison & Glaz Confidence Intervalscimp_wald
: Wald Confidence Intervalscimp_waldcc
: Wald Confidence Interval (with continuity correction)scimp_wilson
: Wilson Confidence Intervalscimple_ci
: Calculate multiple simultaneous confidence intervals using selected methods (excluding Bayesian methods)scimple_short_to_long
: Simple tranlsation table from method shorthand to full method nameThereâs also a handy named vector scimple_short_to_long
which you can use to expand shorthand method names (e.g. âsgâ) to long (e.g. âSison & Glazâ).
Package installation:
install.packages("scimple", repos = c("https://cinc.rud.is", "https://cloud.r-project.org/")) # or remotes::install_git("https://git.rud.is/hrbrmstr/scimple.git") # or remotes::install_git("https://git.sr.ht/~hrbrmstr/scimple") # or remotes::install_gitlab("hrbrmstr/scimple") # or remotes::install_bitbucket("hrbrmstr/scimple") # or remotes::install_github("hrbrmstr/scimple")
NOTE: To use the âremotesâ install options you will need to have the {remotes} package installed.
library(scimple) library(hrbrthemes) library(tidyverse) y <- c(44, 55, 43, 32, 67, 78) z <- 0.05 scimple_ci(y, z) %>% mutate(method=scimple_short_to_long[method]) -> cis print(cis) ## # A tibble: 42 x 8 ## method lower_limit upper_limit adj_ll adj_ul volume inpmat alpha ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 Fitzpatrick & Scott 0.0831 0.193 0.0831 0.193 0.00000175 44 0.05 ## 2 Fitzpatrick & Scott 0.118 0.227 0.118 0.227 0.00000175 55 0.05 ## 3 Fitzpatrick & Scott 0.0799 0.190 0.0799 0.190 0.00000175 43 0.05 ## 4 Fitzpatrick & Scott 0.0454 0.155 0.0454 0.155 0.00000175 32 0.05 ## 5 Fitzpatrick & Scott 0.155 0.265 0.155 0.265 0.00000175 67 0.05 ## 6 Fitzpatrick & Scott 0.190 0.299 0.190 0.299 0.00000175 78 0.05 ## 7 Goodman 0.0947 0.197 0.0947 0.197 0.00000155 44 0.05 ## 8 Goodman 0.124 0.235 0.124 0.235 0.00000155 55 0.05 ## 9 Goodman 0.0921 0.193 0.0921 0.193 0.00000155 43 0.05 ## 10 Goodman 0.0641 0.154 0.0641 0.154 0.00000155 32 0.05 ## # ⦠with 32 more rows ggplot(cis) + geom_segment(aes(x=lower_limit, xend=upper_limit, y=method, yend=method, color=method)) + scale_color_ipsum(name=NULL) + facet_wrap(~inpmat, scales="free_x") + labs(x=NULL, y=NULL, title="Multipe simultaneous confidence intervals", subtitle="Note free X scale") + theme_ipsum_rc(grid="X", base_size=11) + theme(legend.position="bottom")