R: Plot model coefficients

ggcoef_model {ggstats}

R Documentation

Plot model coefficients

Description

ggcoef_model(), ggcoef_table(), ggcoef_multinom(), ggcoef_multicomponents() 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(),
  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(),
  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),
  plot_title = NULL,
  ...
)

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_multinom(
  model,
  type = c("dodged", "faceted", "table"),
  y.level_label = NULL,
  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(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  return_data = 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_multicomponents(
  model,
  type = c("dodged", "faceted", "table"),
  component_col = "component",
  component_label = NULL,
  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(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  return_data = 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_plot(
  data,
  x = "estimate",
  y = "label",
  exponentiate = FALSE,
  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"
)

Arguments

`model`	a regression model object
`tidy_fun`	option to specify a custom tidier function
`tidy_args`	Additional arguments passed to `broom.helpers::tidy_plus_plus()` and to `tidy_fun`
`conf.int`	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`	a named list or a named vector of custom variable labels
`term_labels`	a named list or a named vector of custom term labels
`interaction_sep`	separator for interaction terms
`categorical_terms_pattern`	a glue pattern for labels of categorical terms with treatment or sum contrasts (see `model_list_terms_levels()`)
`add_reference_rows`	should reference rows be added?
`no_reference_row`	variables (accepts tidyselect notation) for those no reference row should be added, when `add_reference_rows = TRUE`
`intercept`	should the intercept(s) be included?
`include`	variables to include. Accepts tidyselect syntax. Use `-` to remove a variable. Default is `everything()`. See also `all_continuous()`, `all_categorical()`, `all_dichotomous()` and `all_interaction()`
`add_pairwise_contrasts`	apply `tidy_add_pairwise_contrasts()`?
`pairwise_variables`	variables to add pairwise contrasts (accepts tidyselect notation)
`keep_model_terms`	keep original model terms for variables where pairwise contrasts are added? (default is `FALSE`)
`pairwise_reverse`	determines whether to use `"pairwise"` (if `TRUE`) or `"revpairwise"` (if `FALSE`), see `emmeans::contrast()`
`emmeans_args`	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
`plot_title`	an optional plot title
`models`	named list of models
`type`	a dodged plot, a faceted plot or multiple table plots?
`y.level_label`	an optional named vector for labeling `y.level` (see examples)
`component_col`	name of the component column
`component_label`	an optional named vector for labeling components
`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
`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()`

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_compare(): designed for displaying several models on the same plot.
ggcoef_multinom(): a variation of ggcoef_model() adapted to multinomial logistic regressions performed with nnet::multinom().
ggcoef_multicomponents(): a variation of ggcoef_model() adapted to multi-component models such as zero-inflated models or beta regressions. ggcoef_multicomponents() has been tested with pscl::zeroinfl(), pscl::hurdle() and betareg::betareg()
ggcoef_plot(): plot a tidy tibble of coefficients

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 "
) +
  ggplot2::scale_y_discrete(labels = 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)
}



# 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)




# specific function for nnet::multinom models
mod <- nnet::multinom(Species ~ ., data = iris)
ggcoef_multinom(mod, exponentiate = TRUE)
ggcoef_multinom(mod, type = "faceted")
ggcoef_multinom(
  mod,
  type = "faceted",
  y.level_label = c("versicolor" = "versicolor\n(ref: setosa)")
)




library(pscl)
data("bioChemists", package = "pscl")
mod <- zeroinfl(art ~ fem * mar | fem + mar, data = bioChemists)
ggcoef_multicomponents(mod)

ggcoef_multicomponents(mod, type = "f")

ggcoef_multicomponents(mod, type = "t")

ggcoef_multicomponents(
  mod,
  type = "t",
  component_label = c(conditional = "Count", zero_inflated = "Zero-inflated")
)

mod2 <- zeroinfl(art ~ fem + mar | 1, data = bioChemists)
ggcoef_multicomponents(mod2, type = "t")

[Package ggstats version 0.6.0 Index]