Data frame and expression for distribution properties

Parametric, non-parametric, robust, and Bayesian measures of centrality.

Usage

centrality_description(
  data,
  x,
  y,
  type = "parametric",
  conf.level = NULL,
  tr = 0.2,
  digits = 2L,
  ...
)

Arguments

data

A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.

x

The grouping (or independent) variable in data.

y

The response (or outcome or dependent) variable from data.

type

A character specifying the type of statistical approach:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

You can specify just the initial letter.

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.

tr

Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.

digits

Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).

...

Currently ignored.

Details

This function describes a distribution for y variable for each level of the grouping variable in x by a set of indices (e.g., measures of centrality, dispersion, range, skewness, kurtosis, etc.). It additionally returns an expression containing a specified centrality measure. The function internally relies on datawizard::describe_distribution() function.

Centrality measures

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

Type	Measure	Function used
Parametric	mean	datawizard::describe_distribution()
Non-parametric	median	datawizard::describe_distribution()
Robust	trimmed mean	datawizard::describe_distribution()
Bayesian	MAP	datawizard::describe_distribution()

Examples

# for reproducibility
set.seed(123)

# ----------------------- parametric -----------------------

centrality_description(iris, Species, Sepal.Length, type = "parametric")
#> # A tibble: 3 × 12
#>   Species    Sepal.Length std.dev   iqr   min   max skewness kurtosis n.obs
#>   <fct>             <dbl>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl> <int>
#> 1 setosa             5.01   0.352 0.400   4.3   5.8    0.120  -0.253     50
#> 2 versicolor         5.94   0.516 0.7     4.9   7      0.105  -0.533     50
#> 3 virginica          6.59   0.636 0.750   4.9   7.9    0.118   0.0329    50
#>   missing.obs expression n.expression          
#>         <int> <list>     <chr>                 
#> 1           0 <language> "setosa\n(n = 50)"    
#> 2           0 <language> "versicolor\n(n = 50)"
#> 3           0 <language> "virginica\n(n = 50)" 

# ----------------------- non-parametric -------------------

centrality_description(mtcars, am, wt, type = "nonparametric")
#> # A tibble: 2 × 12
#>      am    wt   mad   iqr   min   max skewness kurtosis n.obs missing.obs
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl> <int>       <int>
#> 1     0  3.52 0.452 0.41   2.46  5.42    1.15     1.06     19           0
#> 2     1  2.32 0.682 0.942  1.51  3.57    0.269   -0.654    13           0
#>   expression n.expression 
#>   <list>     <chr>        
#> 1 <language> "0\n(n = 19)"
#> 2 <language> "1\n(n = 13)"

# ----------------------- robust ---------------------------

centrality_description(ToothGrowth, supp, len, type = "robust")
#> # A tibble: 2 × 12
#>   supp    len std.dev   iqr   min   max skewness kurtosis n.obs missing.obs
#>   <fct> <dbl>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl> <int>       <int>
#> 1 OJ     21.7    6.61  10.9   8.2  30.9   -0.580   -0.831    30           0
#> 2 VC     16.6    8.27  12.5   4.2  33.9    0.306   -0.700    30           0
#>   expression n.expression  
#>   <list>     <chr>         
#> 1 <language> "OJ\n(n = 30)"
#> 2 <language> "VC\n(n = 30)"

# ----------------------- Bayesian -------------------------

centrality_description(sleep, group, extra, type = "bayes")
#> # A tibble: 2 × 11
#>   group  extra   iqr   min   max skewness kurtosis n.obs missing.obs expression
#>   <fct>  <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl> <int>       <int> <list>    
#> 1 1     0.0579  2.8   -1.6   3.7    0.581   -0.630    10           0 <language>
#> 2 2     0.973   3.82  -0.1   5.5    0.386   -1.42     10           0 <language>
#>   n.expression 
#>   <chr>        
#> 1 "1\n(n = 10)"
#> 2 "2\n(n = 10)"