ggpmisc (version 0.3.9)

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),
  tidy.args = list(),
  label.x = "left",
  label.y = "top",
  hstep = 0,
  vstep = NULL,
  position = "identity",
  na.rm = FALSE,
  show.legend = FALSE,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

The aesthetic mapping, usually constructed with aes or 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.

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.

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()` especializations 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.

Computed variables

The output of tidy() is returned after reshaping it into a single row. Grouping is respected, and the model fit separatately 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.

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.

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 wich 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 intead 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.

See Also

broom and broom.mixed for details on how the tidying of the result of model fits is done.

Other Statistics calling generic tidier methods.: stat_fit_augment(), stat_fit_glance(), stat_fit_tb()

Examples

# NOT RUN {
library(broom)
library(gginnards)
library(quantreg)

# Regression by panel, exploring computed variables with geom_debug()
ggplot(mtcars, aes(x = disp, y = mpg)) +
  stat_smooth(method = "lm") +
  geom_point(aes(colour = factor(cyl))) +
  stat_fit_tidy(method = "lm",
                method.args = list(formula = y ~ x),
                geom = "debug")

# Regression by panel example
ggplot(mtcars, aes(x = disp, y = mpg)) +
  stat_smooth(method = "lm") +
  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",
                                              stat(x_estimate),
                                              stat(x_p.value))))

# Regression by group example
ggplot(mtcars, aes(x = disp, y = mpg, colour = factor(cyl))) +
  stat_smooth(method = "lm") +
  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",
                                              stat(x_estimate),
                                              stat(x_p.value))))

# Weighted regression example
ggplot(mtcars, aes(x = disp, y = mpg, weight = cyl)) +
  stat_smooth(method = "lm") +
  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",
                                              stat(x_estimate),
                                              stat(x_p.value))))

# Correlation test
ggplot(mtcars, aes(x = disp, y = mpg)) +
  stat_smooth(method = "lm") +
  geom_point() +
  stat_fit_tidy(method = "cor.test",
                label.y = "bottom",
                method.args = list(formula = ~ x + y),
                mapping = aes(label = sprintf("R = %.3g\np-value = %.3g",
                                              stat(`_estimate`),
                                              stat(`_p.value`))))

# Quantile regression
ggplot(mtcars, aes(x = disp, y = mpg)) +
  stat_smooth(method = "lm") +
  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",
                                              stat(x_estimate),
                                              stat(x_p.value))))

# }