stat_fit_tidy: One row data frame with fitted parameter estimates

Description

stat_fit_tidy fits a model and returns a "tidy" version of the model's summary, using 'tidy() methods from packages 'broom', 'broom.mixed', or other sources. To add the summary in tabular form use stat_fit_tb instead of this statistic. When using stat_fit_tidy() you will most likely want to change the default mapping for label.

Usage

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

Value

The output of tidy() is returned after reshaping it into a single row. Grouping is respected, and the model fitted separately to each group of data. The returned data object has one row for each group within a panel. To use the intercept, note that output of tidy() is renamed from (Intercept) to Intercept. Otherwise, the names of the columns in the returned data are based on those returned by the

tidy() method for the model fit class returned by the fit function. These 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. Names of columns as returned by default are not always syntactically valid R names making it necessary to use back ticks to access them. Syntactically valid names are guaranteed if sanitize.names = TRUE is added to the call.

To explore the values returned by this statistic, 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 dataset - 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, tidy.args: list of arguments to pass to method, and to [generics::tidy], 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 or character. Coordinates to be used for positioning the output, expressed in "normalized parent coordinates" or character string. If too short they will be recycled.
hstep, vstep: numeric in npc units, the horizontal and vertical step used between labels for different groups.
sanitize.names: logical If true sanitize column names in the returned data with R's make.names() function.
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_tidy applies the function given by method separately to each group of observations; in ggplot2 factors mapped to aesthetics generate a separate group for each level. Because of this, stat_fit_tidy is not useful for annotating plots with results from t.test() or ANOVA or ANCOVA. In such cases use instead stat_fit_tb() which applies the model fitting per panel.

Details

stat_fit_tidy together with stat_fit_glance 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 '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.
# We check availability before running them to avoid errors.

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)

# This provides a quick way of finding out the names of the variables that
# are available for mapping to aesthetics. This is specially important for
# this stat as these names depend on the specific tidy() method used, which
# depends on the method used, such as lm(), used to fit the model.

# Regression by panel, default column names
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    stat_smooth(method = "lm", formula = y ~ x + I(x^2)) +
    geom_point(aes(colour = factor(cyl))) +
    stat_fit_tidy(method = "lm",
                  method.args = list(formula = y ~ x + I(x^2)),
                  geom = "debug")

# Regression by panel, sanitized column names
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    stat_smooth(method = "lm", formula = y ~ x + I(x^2)) +
    geom_point(aes(colour = factor(cyl))) +
    stat_fit_tidy(method = "lm",
                  method.args = list(formula = y ~ x + I(x^2)),
                  geom = "debug", sanitize.names = TRUE)
  }
}

# Regression by panel example
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    stat_smooth(method = "lm", formula = y ~ x) +
    geom_point(aes(colour = factor(cyl))) +
    stat_fit_tidy(method = "lm",
                  label.x = "right",
                  method.args = list(formula = y ~ x),
                  mapping = aes(label = sprintf("Slope = %.3g\np-value = %.3g",
                                                after_stat(x_estimate),
                                                after_stat(x_p.value))))

# Regression by group example
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg, colour = factor(cyl))) +
    stat_smooth(method = "lm", formula = y ~ x) +
    geom_point() +
    stat_fit_tidy(method = "lm",
                  label.x = "right",
                  method.args = list(formula = y ~ x),
                  mapping = aes(label = sprintf("Slope = %.3g, p-value = %.3g",
                                                after_stat(x_estimate),
                                                after_stat(x_p.value))))

# Weighted regression example
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg, weight = cyl)) +
    stat_smooth(method = "lm", formula = y ~ x) +
    geom_point(aes(colour = factor(cyl))) +
    stat_fit_tidy(method = "lm",
                  label.x = "right",
                  method.args = list(formula = y ~ x, weights = quote(weight)),
                  mapping = aes(label = sprintf("Slope = %.3g\np-value = %.3g",
                                                after_stat(x_estimate),
                                                after_stat(x_p.value))))

# Quantile regression
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    stat_smooth(method = "lm", formula = y ~ x) +
    geom_point() +
    stat_fit_tidy(method = "rq",
                  label.y = "bottom",
                  method.args = list(formula = y ~ x),
                  tidy.args = list(se.type = "nid"),
                  mapping = aes(label = sprintf("Slope = %.3g\np-value = %.3g",
                                                after_stat(x_estimate),
                                                after_stat(x_p.value))))

Run the code above in your browser using DataLab