The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Using {ordinalsimr}

Shiny App

The {ordinalsimr} package wraps a Shiny application and supporting functions for running simulation studies on several pre-selected statistical tests applicable to ordinal data. The Shiny app is particularly suited for calculating Power and Type II error for a proposed 2-group comparison of an ordinal endpoint. Available parameters to manipulate before running the tests include:

Number of simulations to run (integer)
The sample size (integer)
The distribution ratio between group 1 and group 2 (ratio of form X:X)
Group 1 and Group 2 distribution probabilities for levels of the ordinal outcome

In addition to these parameters for running the simulation, the following can be adjusted in the Distributions page

Setting the alpha significance level/p-value threshold
Setting the confidence intervals for the Power/T2 Error calculations
Setting the confidence intervals (separately) for the Type I Error in Group 1 and Group 2

Bug reports and feature requests can be submitted as issues at https://github.com/NeuroShepherd/ordinalsimr/issues

Using Downloaded Data

Data downloaded after running a simulation is stored as a .rds file, and can be loaded into your R session using readRDS(). The data is structured as a named list with 3 elements at the top level, and several sub-elements. A summary of the available information is available in the code below.

output <- readRDS("data-2025-01-19-d8621b-1.rds")
output$comparison_data$distribution_statistics
#> # A tibble: 36 × 10
#> # Groups:   Sample Size [6]
#>    `Sample Size` test   lower_power_bound upper_power_bound power `Power 95% CI`
#>            <int> <chr>              <dbl>             <dbl> <dbl> <chr>         
#>  1            30 Wilco…             0.452             0.736  0.6  [0.452, 0.736]
#>  2            30 Fisher             0.337             0.626  0.48 [0.337, 0.626]
#>  3            30 Chi S…             0.374             0.663  0.52 [0.374, 0.663]
#>  4            30 Chi S…             0.374             0.663  0.52 [0.374, 0.663]
#>  5            30 Prop.…             0.472             0.753  0.62 [0.472, 0.753]
#>  6            30 Coin …             0.472             0.753  0.62 [0.472, 0.753]
#>  7            31 Wilco…             0.512             0.788  0.66 [0.512, 0.788]
#>  8            31 Fisher             0.337             0.626  0.48 [0.337, 0.626]
#>  9            31 Chi S…             0.374             0.663  0.52 [0.374, 0.663]
#> 10            31 Chi S…             0.374             0.663  0.52 [0.374, 0.663]
#> # ℹ 26 more rows
#> # ℹ 4 more variables: lower_t2error_bound <dbl>, upper_t2error_bound <dbl>,
#> #   t2_error <dbl>, `TII Error 95% CI` <chr>
str(output, max.level = 2)
#> List of 3
#>  $ comparison_data:List of 3
#>   ..$ run_info               : tibble [300 × 13] (S3: tbl_df/tbl/data.frame)
#>   ..$ distribution_statistics: gropd_df [36 × 10] (S3: grouped_df/tbl_df/tbl/data.frame)
#>   .. ..- attr(*, "groups")= tibble [6 × 2] (S3: tbl_df/tbl/data.frame)
#>   .. .. ..- attr(*, ".drop")= logi TRUE
#>   ..$ distribution_plot      :List of 11
#>   .. ..- attr(*, "class")= chr [1:2] "gg" "ggplot"
#>  $ group1_data    :List of 2
#>   ..$ run_info      : tibble [300 × 13] (S3: tbl_df/tbl/data.frame)
#>   ..$ group1_t1error: gropd_df [36 × 6] (S3: grouped_df/tbl_df/tbl/data.frame)
#>   .. ..- attr(*, "groups")= tibble [6 × 2] (S3: tbl_df/tbl/data.frame)
#>   .. .. ..- attr(*, ".drop")= logi TRUE
#>  $ group2_data    :List of 2
#>   ..$ run_info      : tibble [300 × 13] (S3: tbl_df/tbl/data.frame)
#>   ..$ group2_t1error: gropd_df [36 × 6] (S3: grouped_df/tbl_df/tbl/data.frame)
#>   .. ..- attr(*, "groups")= tibble [6 × 2] (S3: tbl_df/tbl/data.frame)
#>   .. .. ..- attr(*, ".drop")= logi TRUE

Top level items
- comparison_data are results from comparing Group 1 against Group 2 in the statistical tests (for TII error and power)
- group1_data are results from comparing Group 1 data against itself (for TI error of this group)
- group2_data are results from comparing Group 2 data against itself (for TI error of this group)
Second level items
- run_info are tables of detailed metainformation about the parameters used for each run
- distribution_statistics are tables of computed TII error, power, and associated confidence intervals
- group1_t1error and group2_t1error are tables of TI error and associated confidence intervals

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.