Expressions with statistics for tidy regression data frames

Usage

tidy_model_expressions(
  data,
  statistic = NULL,
  digits = 2L,
  effsize.type = "omega",
  ...
)

Arguments

data: A tidy data frame from regression model object (see statsExpressions::tidy_model_parameters()).
statistic: Which statistic is to be displayed (either "t" or "f"or "z" or "chi") in the expression.
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()).
effsize.type: Type of effect size needed for parametric tests. The argument can be "eta" (partial eta-squared) or "omega" (partial omega-squared).
...: Currently ignored.

Details

When any of the necessary numeric column values (estimate, statistic, p.value) are missing, for these rows, a NULL is returned instead of an expression with empty strings.

Note

This is an experimental function and may change in the future. Please do not use it yet in your workflow.

Examples

# setup
set.seed(123)
library(statsExpressions)

# extract a tidy data frame
df <- tidy_model_parameters(lm(wt ~ am * cyl, mtcars))

# create a column containing expression; the expression will depend on `statistic`
tidy_model_expressions(df, statistic = "t")
#> # A tibble: 4 × 11
#>   term        estimate std.error conf.level conf.low conf.high statistic
#>   <chr>          <dbl>     <dbl>      <dbl>    <dbl>     <dbl>     <dbl>
#> 1 (Intercept)   1.66      0.587        0.95    0.455     2.86      2.82 
#> 2 am           -0.956     0.793        0.95   -2.58      0.668    -1.21 
#> 3 cyl           0.304     0.0826       0.95    0.135     0.473     3.68 
#> 4 am:cyl        0.0328    0.130        0.95   -0.234     0.300     0.252
#>   df.error  p.value conf.method expression
#>      <int>    <dbl> <chr>       <list>    
#> 1       28 0.00864  Wald        <language>
#> 2       28 0.238    Wald        <language>
#> 3       28 0.000989 Wald        <language>
#> 4       28 0.803    Wald        <language>
tidy_model_expressions(df, statistic = "z")
#> # A tibble: 4 × 11
#>   term        estimate std.error conf.level conf.low conf.high statistic
#>   <chr>          <dbl>     <dbl>      <dbl>    <dbl>     <dbl>     <dbl>
#> 1 (Intercept)   1.66      0.587        0.95    0.455     2.86      2.82 
#> 2 am           -0.956     0.793        0.95   -2.58      0.668    -1.21 
#> 3 cyl           0.304     0.0826       0.95    0.135     0.473     3.68 
#> 4 am:cyl        0.0328    0.130        0.95   -0.234     0.300     0.252
#>   df.error  p.value conf.method expression
#>      <int>    <dbl> <chr>       <list>    
#> 1       28 0.00864  Wald        <language>
#> 2       28 0.238    Wald        <language>
#> 3       28 0.000989 Wald        <language>
#> 4       28 0.803    Wald        <language>
tidy_model_expressions(df, statistic = "chi")
#> # A tibble: 4 × 11
#>   term        estimate std.error conf.level conf.low conf.high statistic
#>   <chr>          <dbl>     <dbl>      <dbl>    <dbl>     <dbl>     <dbl>
#> 1 (Intercept)   1.66      0.587        0.95    0.455     2.86      2.82 
#> 2 am           -0.956     0.793        0.95   -2.58      0.668    -1.21 
#> 3 cyl           0.304     0.0826       0.95    0.135     0.473     3.68 
#> 4 am:cyl        0.0328    0.130        0.95   -0.234     0.300     0.252
#>   df.error  p.value conf.method expression
#>      <int>    <dbl> <chr>       <list>    
#> 1       28 0.00864  Wald        <language>
#> 2       28 0.238    Wald        <language>
#> 3       28 0.000989 Wald        <language>
#> 4       28 0.803    Wald        <language>