This functions takes a data frame with model parameters as input and formats certain columns into a more readable layout (like collapsing separate columns for lower and upper confidence interval values). Furthermore, column names are formatted as well. Note that format_table() converts all columns into character vectors!

## Usage

format_table(
x,
pretty_names = TRUE,
stars = FALSE,
digits = 2,
ci_width = "auto",
ci_brackets = TRUE,
ci_digits = 2,
p_digits = 3,
rope_digits = 2,
ic_digits = 1,
zap_small = FALSE,
preserve_attributes = FALSE,
exact = TRUE,
use_symbols = getOption("insight_use_symbols", FALSE),
verbose = TRUE,
...
)

## Arguments

x

A data frame of model's parameters, as returned by various functions of the easystats-packages. May also be a result from broom::tidy().

pretty_names

Return "pretty" (i.e. more human readable) parameter names.

stars

If TRUE, add significance stars (e.g., p < .001***). Can also be a character vector, naming the columns that should include stars for significant values. This is especially useful for Bayesian models, where we might have multiple columns with significant values, e.g. BF for the Bayes factor or pd for the probability of direction. In such cases, use stars = c("pd", "BF") to add stars to both columns, or stars = "BF" to only add stars to the Bayes factor and exclude the pd column. Currently, following columns are recognized: "BF", "pd" and "p".

digits, ci_digits, p_digits, rope_digits, ic_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()).

ci_width

Minimum width of the returned string for confidence intervals. If not NULL and width is larger than the string's length, leading whitespaces are added to the string. If width="auto", width will be set to the length of the longest string.

ci_brackets

Logical, if TRUE (default), CI-values are encompassed in square brackets (else in parentheses).

zap_small

Logical, if TRUE, small values are rounded after digits decimal places. If FALSE, values with more decimal places than digits are printed in scientific notation.

preserve_attributes

Logical, if TRUE, preserves all attributes from the input data frame.

exact

Formatting for Bayes factor columns, in case the provided data frame contains such a column (i.e. columns named "BF" or "log_BF"). For exact = TRUE, very large or very small values are then either reported with a scientific format (e.g., 4.24e5), else as truncated values (as "> 1000" and "< 1/1000").

use_symbols

Logical, if TRUE, column names that refer to particular effectsizes (like Phi, Omega or Epsilon) include the related unicode-character instead of the written name. This only works on Windows for R >= 4.2, and on OS X or Linux for R >= 4.0. It is possible to define a global option for this setting, see 'Note'.

verbose

Toggle messages and warnings.

...

Arguments passed to or from other methods.

## Value

A data frame. Note that format_table() converts all columns into character vectors!

## Note

options(insight_use_symbols = TRUE) override the use_symbols argument and always displays symbols, if possible.

## Examples

format_table(head(iris), digits = 1)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa
#> 6          5.4         3.9          1.7         0.4  setosa

if (require("parameters")) {
x <- model_parameters(lm(Sepal.Length ~ Species * Sepal.Width, data = iris))
as.data.frame(format_table(x))
as.data.frame(format_table(x, p_digits = "scientific"))
}
#>
#> Attaching package: ‘parameters’
#> The following object is masked from ‘package:rstanarm’:
#>
#>     compare_models
#>                            Parameter Coefficient   SE        95% CI t(144)
#> 1                        (Intercept)        2.64 0.57 [ 1.51, 3.77]   4.62
#> 2               Species [versicolor]        0.90 0.80 [-0.68, 2.48]   1.13
#> 3                Species [virginica]        1.27 0.82 [-0.35, 2.88]   1.55
#> 4                        Sepal Width        0.69 0.17 [ 0.36, 1.02]   4.17
#> 5 Species [versicolor] × Sepal Width        0.17 0.26 [-0.34, 0.69]   0.67
#> 6  Species [virginica] × Sepal Width        0.21 0.26 [-0.29, 0.72]   0.83
#>             p
#> 1 8.52612e-06
#> 2 2.61332e-01
#> 3 1.22515e-01
#> 4 5.31104e-05
#> 5 5.02805e-01
#> 6 4.10634e-01
# \donttest{
if (require("rstanarm", warn.conflicts = FALSE) &&
require("parameters", , warn.conflicts = FALSE)) {
model <- stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, seed = 123)
x <- model_parameters(model, ci = c(0.69, 0.89, 0.95))
as.data.frame(format_table(x))
}
#>           Parameter Median       69% CI       89% CI       95% CI   pd  Rhat
#> 1       (Intercept)   5.01 [4.93, 5.08] [4.86, 5.15] [4.88, 5.12] 100% 1.000
#> 2 Speciesversicolor   0.93 [0.82, 1.04] [0.73, 1.14] [0.76, 1.11] 100% 1.000
#> 3  Speciesvirginica   1.58 [1.48, 1.69] [1.39, 1.79] [1.42, 1.75] 100% 1.000
#>       ESS                 Prior
#> 1 3279.00 Normal (5.84 +- 2.07)
#> 2 3458.00 Normal (0.00 +- 4.38)
#> 3 3201.00 Normal (0.00 +- 4.38)
# }