scimple

Tidy Simultaneous Confidence Intervals for Multinomial Proportions

Description

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.

What’s Inside The Tin

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 Interval
• scimp_goodman: Goodman Confidence Interval
• scimp_qh: Quesenberry & Hurst Confidence Interval
• scimp_sg: Sison & Glaz Confidence Interval
• scimp_wald: Wald Confidence Interval
• scimp_waldcc: Wald Confidence Interval (with continuity correction)
• scimp_wilson: Wilson Confidence Interval
• scimple_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 name

There’s also a handy named vector scimple_short_to_long which you can use to expand shorthand method names (e.g. “sg”) to long (e.g. “Sison & Glaz”).

Installation

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 ‘remotes’ install options you will need to have the {remotes} package installed.

Usage

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")

scimple Metrics

Lang	# Files	(%)	LoC	(%)	Blank lines	(%)	# Lines	(%)
R	16	0.94	438	0.95	104	0.85	185	0.86
Rmd	1	0.06	23	0.05	19	0.15	29	0.14

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.