Print and format ggeffects-objects — format.ggeffects • ggeffects

A generic print-method for ggeffects-objects.

Usage

# S3 method for class 'ggeffects'
format(
  x,
  variable_labels = FALSE,
  value_labels = FALSE,
  group_name = FALSE,
  row_header_separator = ", ",
  digits = 2,
  collapse_ci = FALSE,
  collapse_tables = FALSE,
  n,
  ...
)

# S3 method for class 'ggcomparisons'
format(x, collapse_ci = FALSE, collapse_p = FALSE, combine_levels = FALSE, ...)

# S3 method for class 'ggeffects'
print(x, group_name = TRUE, digits = 2, verbose = TRUE, ...)

# S3 method for class 'ggeffects'
print_md(x, group_name = TRUE, digits = 2, ...)

# S3 method for class 'ggeffects'
print_html(
  x,
  group_name = TRUE,
  digits = 2,
  theme = NULL,
  engine = c("tt", "gt"),
  ...
)

# S3 method for class 'ggcomparisons'
print(x, collapse_tables = FALSE, ...)

# S3 method for class 'ggcomparisons'
print_html(
  x,
  collapse_ci = FALSE,
  collapse_p = FALSE,
  theme = NULL,
  engine = c("tt", "gt"),
  ...
)

# S3 method for class 'ggcomparisons'
print_md(x, collapse_ci = FALSE, collapse_p = FALSE, theme = NULL, ...)

Arguments

x: An object of class ggeffects, as returned by the functions from this package.
variable_labels: Logical, if TRUE variable labels are used as column headers. If FALSE, variable names are used.
value_labels: Logical, if TRUE, value labels are used as values in the table output. If FALSE, the numeric values or factor levels are used.
group_name: Logical, if TRUE, the name of further focal terms are used in the sub-headings of the table. If FALSE, only the values of the focal terms are used.
row_header_separator: Character, separator between the different subgroups in the table output.
digits: Number of digits to print.
collapse_ci: Logical, if TRUE, the columns with predicted values and confidence intervals are collapsed into one column, e.g. Predicted (95% CI).
collapse_tables: Logical, if TRUE, all tables are combined into one. The tables are not split by further focal terms, but rather are added as columns. Only works when there is more than one focal term.
n: Number of rows to print per subgroup. If NULL, a default number of rows is printed, depending on the number of subgroups.
...: Further arguments passed down to format.ggeffects(), some of them are also passed down further to insight::format_table() or insight::format_value().
collapse_p: Logical, if TRUE, the columns with predicted values and p-values are collapsed into one column, where significant p-values are indicated as asterisks.
combine_levels: Logical, if TRUE, the levels of the first comparison of each focal term against the second are combined into one column. This is useful when comparing multiple focal terms, e.g. education = low-high and gender = male-female are combined into first = low-male and second = high-female.
verbose: Toggle messages.
theme: The theme to apply to the table. One of "grid", "striped", "bootstrap", or "darklines".
engine: The engine to use for printing. One of "tt" (default) or "gt". "tt" uses the tinytable package, "gt" uses the gt package.

Value

format() return a formatted data frame, print() prints a formatted data frame to the console. print_html() returns a tinytable object by default (unless changed with engine = "gt"), which is printed as HTML, markdown or LaTeX table (depending on the context from which print_html() is called, see tinytable::tt() for details).

Global Options to Customize Tables when Printing

The verbose argument can be used to display or silence messages and warnings. Furthermore, options() can be used to set defaults for the print() and print_html() method. The following options are available, which can simply be run in the console:

ggeffects_ci_brackets: Define a character vector of length two, indicating the opening and closing parentheses that encompass the confidence intervals values, e.g. options(ggeffects_ci_brackets = c("[", "]")).
ggeffects_collapse_ci: Logical, if TRUE, the columns with predicted values (or contrasts) and confidence intervals are collapsed into one column, e.g. options(ggeffects_collapse_ci = TRUE).
ggeffects_collapse_p: Logical, if TRUE, the columns with predicted values (or contrasts) and p-values are collapsed into one column, e.g. options(ggeffects_collapse_p = TRUE). Note that p-values are replaced by asterisk-symbols (stars) or empty strings when ggeffects_collapse_p = TRUE, depending on the significance level.
ggeffects_collapse_tables: Logical, if TRUE, multiple tables for subgroups are combined into one table. Only works when there is more than one focal term, e.g. options(ggeffects_collapse_tables = TRUE).
ggeffects_output_format: String, either "text", "markdown" or "html". Defines the default output format from predict_response(). If "html", a formatted HTML table is created and printed to the view pane. "markdown" creates a markdown-formatted table inside Rmarkdown documents, and prints a text-format table to the console when used interactively. If "text" or NULL, a formatted table is printed to the console, e.g. options(ggeffects_output_format = "html").
ggeffects_html_engine: String, either "tt" or "gt". Defines the default engine to use for printing HTML tables. If "tt", the tinytable package is used, if "gt", the gt package is used, e.g. options(ggeffects_html_engine = "gt").

Use options(<option_name> = NULL) to remove the option.

Examples

data(efc, package = "ggeffects")
fit <- lm(barthtot ~ c12hour + e42dep, data = efc)

# default print
predict_response(fit, "e42dep")
#> # Predicted values of Total score BARTHEL INDEX
#> 
#> e42dep | Predicted |         95% CI
#> -----------------------------------
#>      1 |    104.42 | 101.21, 107.64
#>      2 |     83.81 |  81.91,  85.72
#>      3 |     63.20 |  61.94,  64.47
#>      4 |     42.59 |  40.54,  44.65
#> 
#> Adjusted for:
#> * c12hour = 42.25

# surround CI values with parentheses
print(predict_response(fit, "e42dep"), ci_brackets = c("(", ")"))
#> # Predicted values of Total score BARTHEL INDEX
#> 
#> e42dep | Predicted |           95% CI
#> -------------------------------------
#>      1 |    104.42 | (101.21, 107.64)
#>      2 |     83.81 | ( 81.91,  85.72)
#>      3 |     63.20 | ( 61.94,  64.47)
#>      4 |     42.59 | ( 40.54,  44.65)
#> 
#> Adjusted for:
#> * c12hour = 42.25
# you can also use `options(ggeffects_ci_brackets = c("[", "]"))`
# to set this globally

# collapse CI columns into column with predicted values
print(predict_response(fit, "e42dep"), collapse_ci = TRUE)
#> # Predicted values of Total score BARTHEL INDEX
#> 
#> e42dep |      Predicted (95% CI)
#> --------------------------------
#>      1 | 104.42 (101.21, 107.64)
#>      2 |  83.81  (81.91,  85.72)
#>      3 |  63.20  (61.94,  64.47)
#>      4 |  42.59  (40.54,  44.65)
#> 
#> Adjusted for:
#> * c12hour = 42.25

# include value labels
print(predict_response(fit, "e42dep"), value_labels = TRUE)
#> # Predicted values of Total score BARTHEL INDEX
#> 
#>                   e42dep | Predicted |         95% CI
#> -----------------------------------------------------
#>          [1] independent |    104.42 | 101.21, 107.64
#>   [2] slightly dependent |     83.81 |  81.91,  85.72
#> [3] moderately dependent |     63.20 |  61.94,  64.47
#>   [4] severely dependent |     42.59 |  40.54,  44.65
#> 
#> Adjusted for:
#> * c12hour = 42.25

# include variable labels in column headers
print(predict_response(fit, "e42dep"), variable_labels = TRUE)
#> # Predicted values of Total score BARTHEL INDEX
#> 
#> elder's dependency | Predicted values of Total score BARTHEL INDEX |         95% CI
#> -----------------------------------------------------------------------------------
#>                  1 |                                        104.42 | 101.21, 107.64
#>                  2 |                                         83.81 |  81.91,  85.72
#>                  3 |                                         63.20 |  61.94,  64.47
#>                  4 |                                         42.59 |  40.54,  44.65
#> 
#> Adjusted for:
#> * c12hour = 42.25

# include value labels and variable labels
print(predict_response(fit, "e42dep"), variable_labels = TRUE, value_labels = TRUE)
#> # Predicted values of Total score BARTHEL INDEX
#> 
#>       elder's dependency | Predicted values of Total score BARTHEL INDEX |         95% CI
#> -----------------------------------------------------------------------------------------
#>          [1] independent |                                        104.42 | 101.21, 107.64
#>   [2] slightly dependent |                                         83.81 |  81.91,  85.72
#> [3] moderately dependent |                                         63.20 |  61.94,  64.47
#>   [4] severely dependent |                                         42.59 |  40.54,  44.65
#> 
#> Adjusted for:
#> * c12hour = 42.25

data(iris)
m <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)

# default print with subgroups
predict_response(m, c("Petal.Length", "Species"))
#> # Predicted values of Sepal.Length
#> 
#> Species: setosa
#> 
#> Petal.Length | Predicted |      95% CI
#> --------------------------------------
#>         1.00 |      4.76 | 4.49,  5.03
#>         2.00 |      5.30 | 4.99,  5.61
#>         3.00 |      5.84 | 4.99,  6.69
#>         4.00 |      6.38 | 4.99,  7.77
#>         5.00 |      6.92 | 4.99,  8.86
#>         7.00 |      8.01 | 4.98, 11.04
#> 
#> Species: versicolor
#> 
#> Petal.Length | Predicted |      95% CI
#> --------------------------------------
#>         1.00 |      3.24 | 2.57,  3.90
#>         2.00 |      4.06 | 3.60,  4.53
#>         3.00 |      4.89 | 4.62,  5.16
#>         4.00 |      5.72 | 5.61,  5.83
#>         5.00 |      6.55 | 6.37,  6.73
#>         7.00 |      8.21 | 7.64,  8.77
#> 
#> Species: virginica
#> 
#> Petal.Length | Predicted |      95% CI
#> --------------------------------------
#>         1.00 |      2.06 | 1.27,  2.84
#>         2.00 |      3.05 | 2.43,  3.67
#>         3.00 |      4.05 | 3.60,  4.50
#>         4.00 |      5.04 | 4.76,  5.33
#>         5.00 |      6.04 | 5.90,  6.17
#>         7.00 |      8.03 | 7.76,  8.30
#> 
#> 
#> Not all rows are shown in the output. Use `print(..., n = Inf)` to show
#>   all rows.

# omit name of grouping variable in subgroup table headers
print(predict_response(m, c("Petal.Length", "Species")), group_name = FALSE)
#> # Predicted values of Sepal.Length
#> 
#> setosa
#> 
#> Petal.Length | Predicted |      95% CI
#> --------------------------------------
#>         1.00 |      4.76 | 4.49,  5.03
#>         2.00 |      5.30 | 4.99,  5.61
#>         3.00 |      5.84 | 4.99,  6.69
#>         4.00 |      6.38 | 4.99,  7.77
#>         5.00 |      6.92 | 4.99,  8.86
#>         7.00 |      8.01 | 4.98, 11.04
#> 
#> versicolor
#> 
#> Petal.Length | Predicted |      95% CI
#> --------------------------------------
#>         1.00 |      3.24 | 2.57,  3.90
#>         2.00 |      4.06 | 3.60,  4.53
#>         3.00 |      4.89 | 4.62,  5.16
#>         4.00 |      5.72 | 5.61,  5.83
#>         5.00 |      6.55 | 6.37,  6.73
#>         7.00 |      8.21 | 7.64,  8.77
#> 
#> virginica
#> 
#> Petal.Length | Predicted |      95% CI
#> --------------------------------------
#>         1.00 |      2.06 | 1.27,  2.84
#>         2.00 |      3.05 | 2.43,  3.67
#>         3.00 |      4.05 | 3.60,  4.50
#>         4.00 |      5.04 | 4.76,  5.33
#>         5.00 |      6.04 | 5.90,  6.17
#>         7.00 |      8.03 | 7.76,  8.30
#> 
#> 
#> Not all rows are shown in the output. Use `print(..., n = Inf)` to show
#>   all rows.

# collapse tables into one
print(predict_response(m, c("Petal.Length", "Species")), collapse_tables = TRUE, n = 3)
#> # Predicted values of Sepal.Length
#> 
#> Petal.Length |    Species | Predicted |      95% CI
#> ---------------------------------------------------
#>         1.00 |     setosa |      4.76 | 4.49,  5.03
#>         3.00 |            |      5.84 | 4.99,  6.69
#>         7.00 |            |      8.01 | 4.98, 11.04
#>         1.00 | versicolor |      3.24 | 2.57,  3.90
#>         3.00 |            |      4.89 | 4.62,  5.16
#>         7.00 |            |      8.21 | 7.64,  8.77
#>         1.00 |  virginica |      2.06 | 1.27,  2.84
#>         3.00 |            |      4.05 | 3.60,  4.50
#>         7.00 |            |      8.03 | 7.76,  8.30
#> 
#> 
#> Not all rows are shown in the output. Use `print(..., n = Inf)` to show
#>   all rows.

# increase number of digits
print(predict_response(fit, "e42dep"), digits = 5)
#> # Predicted values of Total score BARTHEL INDEX
#> 
#> e42dep | Predicted |               95% CI
#> -----------------------------------------
#>      1 | 104.42339 | 101.21081, 107.63598
#>      2 |  83.81330 |  81.90728,  85.71932
#>      3 |  63.20320 |  61.93656,  64.46985
#>      4 |  42.59311 |  40.53516,  44.65105
#> 
#> Adjusted for:
#> * c12hour = 42.25