Parameter table formatting

This functions takes a data frame (usually 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,
  stars_only = FALSE,
  digits = 2,
  ci_width = "auto",
  ci_brackets = TRUE,
  ci_digits = digits,
  p_digits = 3,
  rope_digits = digits,
  ic_digits = 1,
  zap_small = FALSE,
  preserve_attributes = FALSE,
  exact = TRUE,
  use_symbols = getOption("insight_use_symbols", FALSE),
  select = NULL,
  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".

stars_only

If TRUE, return significant stars only (and no p-values).

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'.

select

Determines which columns are printed and the table layout. There are two options for this argument:

A string expression with layout pattern

select is a string with "tokens" enclosed in braces. These tokens will be replaced by their associated columns, where the selected columns will be collapsed into one column. Following tokens are replaced by the related coefficients or statistics: {estimate}, {se}, {ci} (or {ci_low} and {ci_high}), {p}, {pd} and {stars}. The token {ci} will be replaced by {ci_low}, {ci_high}. Example: select = "{estimate}{stars} ({ci})"

It is possible to create multiple columns as well. A | separates values into new cells/columns. Example: select = "{estimate} ({ci})|{p}".
A string indicating a pre-defined layout

select can be one of the following string values, to create one of the following pre-defined column layouts:
- "minimal": Estimates, confidence intervals and numeric p-values, in two columns. This is equivalent to select = "{estimate} ({ci})|{p}".
- "short": Estimate, standard errors and numeric p-values, in two columns. This is equivalent to select = "{estimate} ({se})|{p}".
- "ci": Estimates and confidence intervals, no asterisks for p-values. This is equivalent to select = "{estimate} ({ci})".
- "se": Estimates and standard errors, no asterisks for p-values. This is equivalent to select = "{estimate} ({se})".
- "ci_p": Estimates, confidence intervals and asterisks for p-values. This is equivalent to select = "{estimate}{stars} ({ci})".
- "se_p": Estimates, standard errors and asterisks for p-values. This is equivalent to select = "{estimate}{stars} ({se})"..

Using select to define columns will re-order columns and remove all columns related to uncertainty (standard errors, confidence intervals), test statistics, and p-values (and similar, like pd or BF for Bayesian models), because these are assumed to be included or intentionally excluded when using select. The new column order will be: Parameter columns first, followed by the "glue" columns, followed by all remaining columns. If further columns should also be placed first, add those as focal_terms attributes to x. I.e., following columns are considers as "parameter columns" and placed first: c(easystats_columns("parameter"), attributes(x)$focal_terms).

Note: glue-like syntax is still experimental in the case of more complex models (like mixed models) and may not return expected results.

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) overrides 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

m <- lm(Sepal.Length ~ Species * Sepal.Width, data = iris)
x <- parameters::model_parameters(m)
as.data.frame(format_table(x))
#>                            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 < .001
#> 2 0.261 
#> 3 0.123 
#> 4 < .001
#> 5 0.503 
#> 6 0.411 
as.data.frame(format_table(x, p_digits = "scientific"))
#>                            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
# "glue" columns
as.data.frame(format_table(x, select = "minimal"))
#>                            Parameter   Coefficient (CI)      p
#> 1                        (Intercept) 2.64 ( 1.51, 3.77) <0.001
#> 2               Species [versicolor] 0.90 (-0.68, 2.48)  0.261
#> 3                Species [virginica] 1.27 (-0.35, 2.88)  0.123
#> 4                        Sepal Width 0.69 ( 0.36, 1.02) <0.001
#> 5 Species [versicolor] × Sepal Width 0.17 (-0.34, 0.69)  0.503
#> 6  Species [virginica] × Sepal Width 0.21 (-0.29, 0.72)  0.411
as.data.frame(format_table(x, select = "{estimate}{stars}|{p}"))
#>                            Parameter Coefficient      p
#> 1                        (Intercept)     2.64*** <0.001
#> 2               Species [versicolor]        0.90  0.261
#> 3                Species [virginica]        1.27  0.123
#> 4                        Sepal Width     0.69*** <0.001
#> 5 Species [versicolor] × Sepal Width        0.17  0.503
#> 6  Species [virginica] × Sepal Width        0.21  0.411

# \donttest{
model <- rstanarm::stan_glm(
  Sepal.Length ~ Species,
  data = iris,
  refresh = 0,
  seed = 123
)
x <- parameters::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)
# }

Usage

Arguments

Value

Note

See also

Examples