ggcoef_model: Plot model coefficients

In ggstats: Extension to 'ggplot2' for Plotting Stats

Plot model coefficients

Description

ggcoef_model(), ggcoef_table(), ggcoef_dodged(), ggcoef_faceted() and ggcoef_compare() use broom.helpers::tidy_plus_plus() to obtain a tibble of the model coefficients, apply additional data transformation and then pass the produced tibble to ggcoef_plot() to generate the plot.

Usage

ggcoef_model(
  model,
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  tidy_args = NULL,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  group_by = broom.helpers::auto_group_by(),
  group_labels = NULL,
  add_pairwise_contrasts = FALSE,
  pairwise_variables = broom.helpers::all_categorical(),
  keep_model_terms = FALSE,
  pairwise_reverse = TRUE,
  emmeans_args = list(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  show_p_values = TRUE,
  signif_stars = TRUE,
  return_data = FALSE,
  ...
)

ggcoef_table(
  model,
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  tidy_args = NULL,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  group_by = broom.helpers::auto_group_by(),
  group_labels = NULL,
  add_pairwise_contrasts = FALSE,
  pairwise_variables = broom.helpers::all_categorical(),
  keep_model_terms = FALSE,
  pairwise_reverse = TRUE,
  emmeans_args = list(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  show_p_values = FALSE,
  signif_stars = FALSE,
  table_stat = c("estimate", "ci", "p.value"),
  table_header = NULL,
  table_text_size = 3,
  table_stat_label = NULL,
  ci_pattern = "{conf.low}, {conf.high}",
  table_witdhs = c(3, 2),
  ...
)

ggcoef_dodged(
  model,
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  tidy_args = NULL,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  group_by = broom.helpers::auto_group_by(),
  group_labels = NULL,
  significance = 1 - conf.level,
  significance_labels = NULL,
  return_data = FALSE,
  ...
)

ggcoef_faceted(
  model,
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  tidy_args = NULL,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  group_by = broom.helpers::auto_group_by(),
  group_labels = NULL,
  significance = 1 - conf.level,
  significance_labels = NULL,
  return_data = FALSE,
  ...
)

ggcoef_compare(
  models,
  type = c("dodged", "faceted"),
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  tidy_args = NULL,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  add_pairwise_contrasts = FALSE,
  pairwise_variables = broom.helpers::all_categorical(),
  keep_model_terms = FALSE,
  pairwise_reverse = TRUE,
  emmeans_args = list(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  return_data = FALSE,
  ...
)

ggcoef_plot(
  data,
  x = "estimate",
  y = "label",
  exponentiate = FALSE,
  y_labeller = NULL,
  point_size = 2,
  point_stroke = 2,
  point_fill = "white",
  colour = NULL,
  colour_guide = TRUE,
  colour_lab = "",
  colour_labels = ggplot2::waiver(),
  shape = "significance",
  shape_values = c(16, 21),
  shape_guide = TRUE,
  shape_lab = "",
  errorbar = TRUE,
  errorbar_height = 0.1,
  errorbar_coloured = FALSE,
  stripped_rows = TRUE,
  strips_odd = "#11111111",
  strips_even = "#00000000",
  vline = TRUE,
  vline_colour = "grey50",
  dodged = FALSE,
  dodged_width = 0.8,
  facet_row = "var_label",
  facet_col = NULL,
  facet_labeller = "label_value",
  plot_title = NULL
)

Arguments

model	a regression model object
tidy_fun	(function) Option to specify a custom tidier function.
tidy_args	Additional arguments passed to broom.helpers::tidy_plus_plus() and to tidy_fun
conf.int	(logical) Should confidence intervals be computed? (see broom::tidy())
conf.level	the confidence level to use for the confidence interval if conf.int = TRUE; must be strictly greater than 0 and less than 1; defaults to 0.95, which corresponds to a 95 percent confidence interval
exponentiate	if TRUE a logarithmic scale will be used for x-axis
variable_labels	(formula-list-selector) A named list or a named vector of custom variable labels.
term_labels	(list or vector) A named list or a named vector of custom term labels.
interaction_sep	(string) Separator for interaction terms.
categorical_terms_pattern	(glue pattern) A glue pattern for labels of categorical terms with treatment or sum contrasts (see model_list_terms_levels()).
add_reference_rows	(logical) Should reference rows be added?
no_reference_row	(tidy-select) Variables for those no reference row should be added, when add_reference_rows = TRUE.
intercept	(logical) Should the intercept(s) be included?
include	(tidy-select) Variables to include. Default is everything(). See also all_continuous(), all_categorical(), all_dichotomous() and all_interaction().
group_by	(tidy-select) One or several variables to group by. Default is auto_group_by(). Use NULL to force ungrouping.
group_labels	(string) An optional named vector of custom term labels.
add_pairwise_contrasts	(logical) Apply tidy_add_pairwise_contrasts()?
pairwise_variables	(tidy-select) Variables to add pairwise contrasts.
keep_model_terms	(logical) Keep original model terms for variables where pairwise contrasts are added? (default is FALSE)
pairwise_reverse	(logical) Determines whether to use "pairwise" (if TRUE) or "revpairwise" (if FALSE), see emmeans::contrast().
emmeans_args	(list) List of additional parameter to pass to emmeans::emmeans() when computing pairwise contrasts.
significance	level (between 0 and 1) below which a coefficient is consider to be significantly different from 0 (or 1 if exponentiate = TRUE), NULL for not highlighting such coefficients
significance_labels	optional vector with custom labels for significance variable
show_p_values	if TRUE, add p-value to labels
signif_stars	if TRUE, add significant stars to labels
return_data	if TRUE, will return the data.frame used for plotting instead of the plot
...	parameters passed to ggcoef_plot()
table_stat	statistics to display in the table, use any column name returned by the tidier or "ci" for confidence intervals formatted according to ci_pattern
table_header	optional custom headers for the table
table_text_size	text size for the table
table_stat_label	optional named list of labeller functions for the displayed statistic (see examples)
ci_pattern	glue pattern for confidence intervals in the table
table_witdhs	relative widths of the forest plot and the coefficients table
models	named list of models
type	a dodged plot, a faceted plot or multiple table plots?
data	a data frame containing data to be plotted, typically the output of ggcoef_model(), ggcoef_compare() or ggcoef_multinom() with the option return_data = TRUE
x, y	variables mapped to x and y axis
y_labeller	optional function to be applied on y labels (see examples)
point_size	size of the points
point_stroke	thickness of the points
point_fill	fill colour for the points
colour	optional variable name to be mapped to colour aesthetic
colour_guide	should colour guide be displayed in the legend?
colour_lab	label of the colour aesthetic in the legend
colour_labels	labels argument passed to ggplot2::scale_colour_discrete() and ggplot2::discrete_scale()
shape	optional variable name to be mapped to the shape aesthetic
shape_values	values of the different shapes to use in ggplot2::scale_shape_manual()
shape_guide	should shape guide be displayed in the legend?
shape_lab	label of the shape aesthetic in the legend
errorbar	should error bars be plotted?
errorbar_height	height of error bars
errorbar_coloured	should error bars be colored as the points?
stripped_rows	should stripped rows be displayed in the background?
strips_odd	color of the odd rows
strips_even	color of the even rows
vline	should a vertical line be drawn at 0 (or 1 if exponentiate = TRUE)?
vline_colour	colour of vertical line
dodged	should points be dodged (according to the colour aesthetic)?
dodged_width	width value for ggplot2::position_dodge()
facet_row	variable name to be used for row facets
facet_col	optional variable name to be used for column facets
facet_labeller	labeller function to be used for labeling facets; if labels are too long, you can use ggplot2::label_wrap_gen() (see examples), more information in the documentation of ggplot2::facet_grid()
plot_title	an optional plot title

Details

For more control, you can use the argument return_data = TRUE to get the produced tibble, apply any transformation of your own and then pass your customized tibble to ggcoef_plot().

Value

A ggplot2 plot or a tibble if return_data = TRUE.

Functions

• ggcoef_table(): a variation of ggcoef_model() adding a table with estimates, confidence intervals and p-values

• ggcoef_dodged(): a dodged variation of ggcoef_model() for multi groups models

• ggcoef_faceted(): a faceted variation of ggcoef_model() for multi groups models

• ggcoef_compare(): designed for displaying several models on the same plot.

• ggcoef_plot(): plot a tidy tibble of coefficients

See Also

vignette("ggcoef_model")

Examples

mod <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris)
ggcoef_model(mod)

ggcoef_table(mod)



ggcoef_table(mod, table_stat = c("estimate", "ci"))

ggcoef_table(
  mod,
  table_stat_label = list(
    estimate = scales::label_number(.001)
  )
)

ggcoef_table(mod, table_text_size = 5, table_witdhs = c(1, 1))

# a logistic regression example
d_titanic <- as.data.frame(Titanic)
d_titanic$Survived <- factor(d_titanic$Survived, c("No", "Yes"))
mod_titanic <- glm(
  Survived ~ Sex * Age + Class,
  weights = Freq,
  data = d_titanic,
  family = binomial
)

# use 'exponentiate = TRUE' to get the Odds Ratio
ggcoef_model(mod_titanic, exponentiate = TRUE)

ggcoef_table(mod_titanic, exponentiate = TRUE)

# display intercepts
ggcoef_model(mod_titanic, exponentiate = TRUE, intercept = TRUE)

# customize terms labels
ggcoef_model(
  mod_titanic,
  exponentiate = TRUE,
  show_p_values = FALSE,
  signif_stars = FALSE,
  add_reference_rows = FALSE,
  categorical_terms_pattern = "{level} (ref: {reference_level})",
  interaction_sep = " x ",
  y_labeller = scales::label_wrap(15)
)

# display only a subset of terms
ggcoef_model(mod_titanic, exponentiate = TRUE, include = c("Age", "Class"))

# do not change points' shape based on significance
ggcoef_model(mod_titanic, exponentiate = TRUE, significance = NULL)

# a black and white version
ggcoef_model(
  mod_titanic,
  exponentiate = TRUE,
  colour = NULL, stripped_rows = FALSE
)

# show dichotomous terms on one row
ggcoef_model(
  mod_titanic,
  exponentiate = TRUE,
  no_reference_row = broom.helpers::all_dichotomous(),
  categorical_terms_pattern =
    "{ifelse(dichotomous, paste0(level, ' / ', reference_level), level)}",
  show_p_values = FALSE
)




data(tips, package = "reshape")
mod_simple <- lm(tip ~ day + time + total_bill, data = tips)
ggcoef_model(mod_simple)

# custom variable labels
# you can use the labelled package to define variable labels
# before computing model
if (requireNamespace("labelled")) {
  tips_labelled <- tips |>
    labelled::set_variable_labels(
      day = "Day of the week",
      time = "Lunch or Dinner",
      total_bill = "Bill's total"
    )
  mod_labelled <- lm(tip ~ day + time + total_bill, data = tips_labelled)
  ggcoef_model(mod_labelled)
}

# you can provide custom variable labels with 'variable_labels'
ggcoef_model(
  mod_simple,
  variable_labels = c(
    day = "Week day",
    time = "Time (lunch or dinner ?)",
    total_bill = "Total of the bill"
  )
)
# if labels are too long, you can use 'facet_labeller' to wrap them
ggcoef_model(
  mod_simple,
  variable_labels = c(
    day = "Week day",
    time = "Time (lunch or dinner ?)",
    total_bill = "Total of the bill"
  ),
  facet_labeller = ggplot2::label_wrap_gen(10)
)

# do not display variable facets but add colour guide
ggcoef_model(mod_simple, facet_row = NULL, colour_guide = TRUE)

# works also with with polynomial terms
mod_poly <- lm(
  tip ~ poly(total_bill, 3) + day,
  data = tips,
)
ggcoef_model(mod_poly)

# or with different type of contrasts
# for sum contrasts, the value of the reference term is computed
if (requireNamespace("emmeans")) {
  mod2 <- lm(
    tip ~ day + time + sex,
    data = tips,
    contrasts = list(time = contr.sum, day = contr.treatment(4, base = 3))
  )
  ggcoef_model(mod2)
}





# multinomial model
mod <- nnet::multinom(grade ~ stage + trt + age, data = gtsummary::trial)
ggcoef_model(mod, exponentiate = TRUE)
ggcoef_table(mod, group_labels = c(II = "Stage 2 vs. 1"))
ggcoef_dodged(mod, exponentiate = TRUE)
ggcoef_faceted(mod, exponentiate = TRUE)




library(pscl)
data("bioChemists", package = "pscl")
mod <- zeroinfl(art ~ fem * mar | fem + mar, data = bioChemists)
ggcoef_model(mod)
ggcoef_table(mod)
ggcoef_dodged(mod)
ggcoef_faceted(
  mod,
  group_labels = c(conditional = "Count", zero_inflated = "Zero-inflated")
)

mod2 <- zeroinfl(art ~ fem + mar | 1, data = bioChemists)
ggcoef_table(mod2)
ggcoef_table(mod2, intercept = TRUE)



# Use ggcoef_compare() for comparing several models on the same plot
mod1 <- lm(Fertility ~ ., data = swiss)
mod2 <- step(mod1, trace = 0)
mod3 <- lm(Fertility ~ Agriculture + Education * Catholic, data = swiss)
models <- list(
  "Full model" = mod1,
  "Simplified model" = mod2,
  "With interaction" = mod3
)

ggcoef_compare(models)
ggcoef_compare(models, type = "faceted")

# you can reverse the vertical position of the point by using a negative
# value for dodged_width (but it will produce some warnings)
ggcoef_compare(models, dodged_width = -.9)

ggstats documentation built on April 12, 2025, 1:29 a.m.