A generic print-method for ggeffects-objects.
# 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 = TRUE, ...)
# 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, ...)An object of class ggeffects, as returned by the functions
from this package.
Logical, if TRUE variable labels are used as column
headers. If FALSE, variable names are used.
Logical, if TRUE, value labels are used as values in
the table output. If FALSE, the numeric values or factor levels are used.
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.
Character, separator between the different subgroups in the table output.
Number of digits to print.
Logical, if TRUE, the columns with predicted values and
confidence intervals are collapsed into one column, e.g. Predicted (95% CI).
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.
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().
Logical, if TRUE, the columns with predicted values and
p-values are collapsed into one column, where significant p-values are
indicated as asterisks.
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.
Toggle messages.
The theme to apply to the table. One of "grid", "striped",
"bootstrap", or "darklines".
The engine to use for printing. One of "tt" (default) or "gt".
"tt" uses the tinytable package, "gt" uses the gt package.
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).
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.
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