stat_fit_tb: Model-fit summary or ANOVA

Description

stat_fit_tb fits a model and returns a "tidy" version of the model's summary or ANOVA table, using package 'broom'. The annotation is added to the plots in tabular form.

Usage

stat_fit_tb(mapping = NULL, data = NULL, geom = "table_npc",
  method = "lm", method.args = list(formula = y ~ x),
  tb.type = "fit.summary", tb.vars = NULL, digits = 3,
  label.x = "center", label.y = "top", label.x.npc = NULL,
  label.y.npc = 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.

method.args

list of arguments to pass to method.

tb.type

character One of "fit.summary", "fit.anova" or "fit.coefs".

tb.vars

character vector, optionally named, used to select and or rename the columns of the table returned.

digits

integer indicating the number of significant digits to be used.

label.x, label.y

numeric Coordinates (in data units) to be used for absolute positioning of the output. If too short they will be recycled.

label.x.npc, label.y.npc

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.

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.

Computed variables

The output of tidy() is returned as a single "cell" in a tibble (i.e. a tibble nested within a tibble). The returned data object contains a single, containing the result from a single model fit to all data in a panel. If grouping is present, it is ignored.

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_tb Applies a model fitting function per panel, using the grouping factors from easthetic mappings in the fitted model. This is suitable, for example for analysis of variance used to test for differences among groups.

The argument to method can be any fit method for which a suitable tidy() method is available, including non-linear regression. Fit methods retain their default arguments unless orverridden.

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.

Examples

Run this code

# NOT RUN {
library(gginnards)
# data for examples
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
covariate <- sqrt(x) + rnorm(9)
group <- factor(c(rep("A", 4), rep("B", 5)))
my.df <- data.frame(x, group, covariate)

# Linear regression
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb() +
  expand_limits(y = 70)

# Linear regression
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb(geom = "debug") +
  expand_limits(y = 70)

# Polynomial regression
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb(method.args = list(formula = y ~ poly(x, 2))) +
  expand_limits(y = 70)

# ANOVA
ggplot(my.df, aes(group, x)) +
  geom_point() +
  stat_fit_tb() +
  expand_limits(y = 70)

# ANOVA with renamed and selected columns
ggplot(my.df, aes(group, x)) +
  geom_point() +
  stat_fit_tb(tb.vars = c(Effect = "term", "italic(F)" = "statistic", "italic(P)" = "p.value"),
              parse = TRUE)

# ANCOVA (covariate not plotted)
ggplot(my.df, aes(group, x, z = covariate)) +
  geom_point() +
  stat_fit_tb(method.args = list(formula = y ~ x + z),
              tb.vars = c(Effect = "term", "italic(F)" = "statistic", "italic(P)" = "p.value"),
              parse = TRUE)

# t-test
ggplot(my.df, aes(group, x)) +
  geom_point() +
  stat_fit_tb(method = "t.test",
              tb.vars = c("italic(t)" = "statistic", "italic(P)" = "p.value"),
              parse = TRUE)

# t-test (equal variances assumed)
ggplot(my.df, aes(group, x)) +
  geom_point() +
  stat_fit_tb(method = "t.test",
              method.args = list(formula = y ~ x, var.equal = TRUE),
              tb.vars = c("italic(t)" = "statistic", "italic(P)" = "p.value"),
              parse = TRUE)

# }

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