stat_fit_glance: One row summary data frame for a fitted model

Description

stat_fit_glance fits a model and returns a "tidy" version of the model's fit, using 'glance() methods from packages 'broom', 'broom.mixed', or other sources.

Usage

stat_fit_glance(
  mapping = NULL,
  data = NULL,
  geom = "text_npc",
  method = "lm",
  method.args = list(formula = y ~ x),
  n.min = 2L,
  glance.args = list(),
  label.x = "left",
  label.y = "top",
  hstep = 0,
  vstep = 0.075,
  position = "identity",
  na.rm = FALSE,
  show.legend = FALSE,
  inherit.aes = TRUE,
  ...
)

Value

The output of the glance() methods is returned almost as is in the data object, as a data frame. The names of the columns in the returned data are consistent with those returned by method glance()

from package 'broom', that will frequently differ from the name of values returned by the print methods corresponding to the fit or test function used. To explore the values returned by this statistic including the name of variables/columns, which vary depending on the model fitting function and model formula we suggest the use of

geom_debug. An example is shown below.

Arguments

mapping: The aesthetic mapping, usually constructed with aes. Only needs to be set at the layer level if you are overriding the plot defaults.
data: A layer specific data set - only needed if you want to override the plot defaults.
geom: The geometric object to use display the data
method: character or function.
method.args, glance.args: list of arguments to pass to method and to [generics::glance()], respectively.
n.min: integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted.
label.x, label.y: numeric with range 0..1 "normalized parent coordinates" (npc units) or character if using geom_text_npc() or geom_label_npc(). If using geom_text() or geom_label() numeric in native data units. If too short they will be recycled.
hstep, vstep: numeric in npc units, the horizontal and vertical step used between labels for different groups.
position: The position adjustment to use for overlapping points on this layer
na.rm: a logical indicating whether NA values should be stripped before the computation proceeds.
show.legend: logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
inherit.aes: If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.
...: other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Warning!

Not all `glance()` methods are defined in package 'broom'. `glance()` specializations for mixed models fits of classes `lme`, `nlme`, `lme4`, and many others are defined in package 'broom.mixed'.

Handling of grouping

stat_fit_glance applies the function given by method separately to each group of observations, and factors mapped to aesthetics, including x and y, create a separate group for each factor level. Because of this, stat_fit_glance is not useful for annotating plots with results from t.test(), ANOVA or ANCOVA. In such cases use the stat_fit_tb() statistic which applies the model fitting per panel.

Model formula required

The current implementation works only with methods that accept a formula as argument and which have a data parameter through which a data frame can be passed. For example, lm() should be used with the formula interface, as the evaluation of x and y needs to be delayed until the internal data object of the ggplot is available. With some methods like stats::cor.test() the data embedded in the "ggplot" object cannot be automatically passed as argument for the data parameter of the test or model fit function. Please, for annotations based on stats::cor.test() use stat_correlation().

Details

stat_fit_glance together with stat_fit_tidy and stat_fit_augment, based on package 'broom' can be used with a broad range of model fitting functions as supported at any given time by package 'broom'. In contrast to stat_poly_eq which can generate text or expression labels automatically, for these functions the mapping of aesthetic label needs to be explicitly supplied in the call, and labels built on the fly.

A ggplot statistic receives as data a data frame that is not the one passed as argument by the user, but instead a data frame with the variables mapped to aesthetics. In other words, it respects the grammar of graphics and consequently within arguments passed through method.args names of aesthetics like $x$ and $y$ should be used instead of the original variable names, while data is automatically passed the data frame. This helps ensure that the model is fitted to the same data as plotted in other layers.

Examples

Run this code

# package 'broom' needs to be installed to run these examples

if (requireNamespace("broom", quietly = TRUE)) {
  broom.installed <- TRUE
  library(broom)
  library(quantreg)

# Inspecting the returned data using geom_debug()
  if (requireNamespace("gginnards", quietly = TRUE)) {
    library(gginnards)

    ggplot(mtcars, aes(x = disp, y = mpg)) +
      stat_smooth(method = "lm") +
      geom_point(aes(colour = factor(cyl))) +
      stat_fit_glance(method = "lm",
                      method.args = list(formula = y ~ x),
                      geom = "debug")
  }
}

if (broom.installed)
# Regression by panel example
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    stat_smooth(method = "lm", formula = y ~ x) +
    geom_point(aes(colour = factor(cyl))) +
    stat_fit_glance(method = "lm",
                    label.y = "bottom",
                    method.args = list(formula = y ~ x),
                    mapping = aes(label = sprintf('italic(r)^2~"="~%.3f~~italic(P)~"="~%.2g',
                                  after_stat(r.squared), after_stat(p.value))),
                    parse = TRUE)

# Regression by group example
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg, colour = factor(cyl))) +
    stat_smooth(method = "lm") +
    geom_point() +
    stat_fit_glance(method = "lm",
                    label.y = "bottom",
                    method.args = list(formula = y ~ x),
                    mapping = aes(label = sprintf('r^2~"="~%.3f~~italic(P)~"="~%.2g',
                                  after_stat(r.squared), after_stat(p.value))),
                    parse = TRUE)

# Weighted regression example
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg, weight = cyl)) +
    stat_smooth(method = "lm") +
    geom_point(aes(colour = factor(cyl))) +
    stat_fit_glance(method = "lm",
                    label.y = "bottom",
                    method.args = list(formula = y ~ x, weights = quote(weight)),
                    mapping = aes(label = sprintf('r^2~"="~%.3f~~italic(P)~"="~%.2g',
                                  after_stat(r.squared), after_stat(p.value))),
                    parse = TRUE)

# correlation test
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    geom_point() +
    stat_fit_glance(method = "cor.test",
                    label.y = "bottom",
                    method.args = list(formula = ~ x + y),
                    mapping = aes(label = sprintf('r[Pearson]~"="~%.3f~~italic(P)~"="~%.2g',
                                  after_stat(estimate), after_stat(p.value))),
                    parse = TRUE)

if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    geom_point() +
    stat_fit_glance(method = "cor.test",
                    label.y = "bottom",
                    method.args = list(formula = ~ x + y, method = "spearman", exact = FALSE),
                    mapping = aes(label = sprintf('r[Spearman]~"="~%.3f~~italic(P)~"="~%.2g',
                                  after_stat(estimate), after_stat(p.value))),
                    parse = TRUE)

# Quantile regression by group example
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    stat_smooth(method = "lm") +
    geom_point() +
    stat_fit_glance(method = "rq",
                    label.y = "bottom",
                    method.args = list(formula = y ~ x),
                    mapping = aes(label = sprintf('AIC = %.3g, BIC = %.3g',
                                  after_stat(AIC), after_stat(BIC))))

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