A print()-method for objects from model_parameters().
# S3 method for parameters_model
format(
x,
pretty_names = TRUE,
split_components = TRUE,
select = NULL,
digits = 2,
ci_digits = digits,
p_digits = 3,
ci_width = NULL,
ci_brackets = NULL,
zap_small = FALSE,
format = NULL,
groups = NULL,
include_reference = FALSE,
...
)# S3 method for parameters_model
print(
x,
pretty_names = TRUE,
split_components = TRUE,
select = NULL,
caption = NULL,
footer = NULL,
digits = 2,
ci_digits = digits,
p_digits = 3,
footer_digits = 3,
show_sigma = FALSE,
show_formula = FALSE,
zap_small = FALSE,
groups = NULL,
column_width = NULL,
ci_brackets = c("[", "]"),
include_reference = FALSE,
...
)
# S3 method for parameters_model
summary(object, ...)
# S3 method for parameters_model
print_html(
x,
pretty_names = TRUE,
split_components = TRUE,
select = NULL,
caption = NULL,
subtitle = NULL,
footer = NULL,
align = NULL,
digits = 2,
ci_digits = digits,
p_digits = 3,
footer_digits = 3,
ci_brackets = c("(", ")"),
show_sigma = FALSE,
show_formula = FALSE,
zap_small = FALSE,
groups = NULL,
font_size = "100%",
line_padding = 4,
column_labels = NULL,
include_reference = FALSE,
verbose = TRUE,
...
)
# S3 method for parameters_model
print_md(
x,
pretty_names = TRUE,
split_components = TRUE,
select = NULL,
caption = NULL,
subtitle = NULL,
footer = NULL,
align = NULL,
digits = 2,
ci_digits = digits,
p_digits = 3,
footer_digits = 3,
ci_brackets = c("(", ")"),
show_sigma = FALSE,
show_formula = FALSE,
zap_small = FALSE,
groups = NULL,
include_reference = FALSE,
verbose = TRUE,
...
)
Invisibly returns the original input object.
An object returned by model_parameters().
Can be TRUE, which will return "pretty" (i.e. more human
readable) parameter names. Or "labels", in which case value and variable
labels will be used as parameters names. The latter only works for "labelled"
data, i.e. if the data used to fit the model had "label" and "labels"
attributes. See also section Global Options to Customize Messages when Printing.
Logical, if TRUE (default), For models with
multiple components (zero-inflation, smooth terms, ...), each component is
printed in a separate table. If FALSE, model parameters are printed
in a single table and a Component column is added to the output.
Determines which columns and and which layout columns are printed. There are three options for this argument:
Selecting columns by name or index
select can be a character vector (or numeric index) of column names that
should be printed. There are two pre-defined options for selecting columns:
select = "minimal" prints coefficients, confidence intervals and p-values,
while select = "short" prints coefficients, standard errors and p-values.
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. However, it is possible to create multiple
columns as well. Following tokens are replaced by the related coefficients
or statistics: {estimate}, {se}, {ci} (or {ci_low} and {ci_high}),
{p} and {stars}. The token {ci} will be replaced by {ci_low}, {ci_high}.
Furthermore, a | separates values into new cells/columns. If
format = "html", a <br> inserts a line break inside a cell. See
'Examples'.
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:
"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})"..
"ci_p2": Estimates, confidence intervals and numeric p-values, in two
columns. This is equivalent to select = "{estimate} ({ci})|{p}".
"se_p2": Estimate, standard errors and numeric p-values, in two columns.
This is equivalent to select = "{estimate} ({se})|{p}".
For model_parameters(), glue-like syntax is still experimental in the
case of more complex models (like mixed models) and may not return expected
results.
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()).
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.
Logical, if TRUE (default), CI-values are
encompassed in square brackets (else in parentheses).
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.
String, indicating the output format. Can be "markdown"
or "html".
Named list, can be used to group parameters in the printed output.
List elements may either be character vectors that match the name of those
parameters that belong to one group, or list elements can be row numbers
of those parameter rows that should belong to one group. The names of the
list elements will be used as group names, which will be inserted as "header
row". A possible use case might be to emphasize focal predictors and control
variables, see 'Examples'. Parameters will be re-ordered according to the
order used in groups, while all non-matching parameters will be added
to the end.
Logical, if TRUE, the reference level of factors will
be added to the parameters table. This is only relevant for models with
categorical predictors. The coefficient for the reference level is always
0 (except when exponentiate = TRUE, then the coefficient will be 1),
so this is just for completeness.
Arguments passed to or from other methods.
Table caption as string. If NULL, depending on the model,
either a default caption or no table caption is printed. Use caption = ""
to suppress the table caption.
Can either be FALSE or an empty string (i.e. "") to
suppress the footer, NULL to print the default footer, or a string. The
latter will combine the string value with the default footer.
Number of decimal places for values in the footer summary.
Logical, if TRUE, adds information about the residual
standard deviation.
Logical, if TRUE, adds the model formula to the output.
Width of table columns. Can be either NULL, a named
numeric vector, or "fixed". If NULL, the width for each table column is
adjusted to the minimum required width. If a named numeric vector, value
names are matched against column names, and for each match, the specified
width is used. If "fixed", and table is split into multiple components,
columns across all table components are adjusted to have the same width.
Table title (same as caption) and subtitle, as strings. If NULL,
no title or subtitle is printed, unless it is stored as attributes (table_title,
or its alias table_caption, and table_subtitle). If x is a list of
data frames, caption may be a list of table captions, one for each table.
Only applies to HTML tables. May be one of "left",
"right" or "center".
For HTML tables, the font size.
For HTML tables, the distance (in pixel) between lines.
Labels of columns for HTML tables. If NULL, automatic
column names are generated. See 'Examples'.
Toggle messages and warnings.
The verbose argument can be used to display or silence messages and
warnings for the different functions in the parameters package. However,
some messages providing additional information can be displayed or suppressed
using options():
parameters_summary: options(parameters_summary = TRUE) will override the
summary argument in model_parameters() and always show the model summary
for non-mixed models.
parameters_mixed_summary: options(parameters_mixed_summary = TRUE) will
override the summary argument in model_parameters() for mixed models, and
will then always show the model summary.
parameters_cimethod: options(parameters_cimethod = TRUE) will show the
additional information about the approximation method used to calculate
confidence intervals and p-values. Set to FALSE to hide this message when
printing model_parameters() objects.
parameters_exponentiate: options(parameters_exponentiate = TRUE) will
show the additional information on how to interpret coefficients of models
with log-transformed response variables or with log-/logit-links when the
exponentiate argument in model_parameters() is not TRUE. Set this option
to FALSE to hide this message when printing model_parameters() objects.
There are further options that can be used to modify the default behaviour for printed outputs:
parameters_labels: options(parameters_labels = TRUE) will use variable
and value labels for pretty names, if data is labelled. If no labels
available, default pretty names are used.
parameters_interaction: options(parameters_interaction = <character>)
will replace the interaction mark (by default, *) with the related character.
parameters_select: options(parameters_select = <value>) will set the
default for the select argument. See argument's documentation for available
options.
easystats_html_engine: options(easystats_html_engine = "gt") will set
the default HTML engine for tables to gt, i.e. the gt package is used to
create HTML tables. If set to tt, the tinytable package is used.
Note that the interpretation of interaction terms depends on many
characteristics of the model. The number of parameters, and overall
performance of the model, can differ or not between a * b
a : b, and a / b, suggesting that sometimes interaction terms
give different parameterizations of the same model, but other times it gives
completely different models (depending on a or b being factors
of covariates, included as main effects or not, etc.). Their interpretation
depends of the full context of the model, which should not be inferred
from the parameters table alone - rather, we recommend to use packages
that calculate estimated marginal means or marginal effects, such as
modelbased, emmeans, ggeffects, or
marginaleffects. To raise awareness for this issue, you may use
print(...,show_formula=TRUE) to add the model-specification to the output
of the print() method for model_parameters().
Throughout the parameters package, we decided to label the residual
degrees of freedom df_error. The reason for this is that these degrees
of freedom not always refer to the residuals. For certain models, they refer
to the estimate error - in a linear model these are the same, but in - for
instance - any mixed effects model, this isn't strictly true. Hence, we
think that df_error is the most generic label for these degrees of
freedom.
summary() is a convenient shortcut for
print(object, select = "minimal", show_sigma = TRUE, show_formula = TRUE).
See also display().
if (FALSE) { # require("gt", quietly = TRUE) && require("glmmTMB", quietly = TRUE)
# \donttest{
library(parameters)
model <- glmmTMB::glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
mp <- model_parameters(model)
print(mp, pretty_names = FALSE)
print(mp, split_components = FALSE)
print(mp, select = c("Parameter", "Coefficient", "SE"))
print(mp, select = "minimal")
# group parameters ------
data(iris)
model <- lm(
Sepal.Width ~ Sepal.Length + Species + Petal.Length,
data = iris
)
# don't select "Intercept" parameter
mp <- model_parameters(model, parameters = "^(?!\\(Intercept)")
groups <- list(
"Focal Predictors" = c("Speciesversicolor", "Speciesvirginica"),
"Controls" = c("Sepal.Length", "Petal.Length")
)
print(mp, groups = groups)
# or use row indices
print(mp, groups = list(
"Focal Predictors" = c(1, 4),
"Controls" = c(2, 3)
))
# only show coefficients, CI and p,
# put non-matched parameters to the end
data(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$gear <- as.factor(mtcars$gear)
model <- lm(mpg ~ hp + gear * vs + cyl + drat, data = mtcars)
# don't select "Intercept" parameter
mp <- model_parameters(model, parameters = "^(?!\\(Intercept)")
print(mp, groups = list(
"Engine" = c("cyl6", "cyl8", "vs", "hp"),
"Interactions" = c("gear4:vs", "gear5:vs")
))
# }
# custom column layouts ------
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
# custom style
result <- compare_parameters(lm1, lm2, select = "{estimate}{stars} ({se})")
print(result)
# \donttest{
# custom style, in HTML
result <- compare_parameters(lm1, lm2, select = "{estimate}({se})|{p}")
print_html(result)
# }
}
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