ggpmisc (version 0.3.0)

stat_fit_glance: One row summary data frame for a fitted model

Description

stat_fit_glance fits a model and returns a summary "glance" of the model's statistics, using package 'broom'.

Usage

stat_fit_glance(mapping = NULL, data = NULL, geom = "null",
  method = "lm", method.args = list(formula = y ~ x),
  label.x.npc = "left", label.y.npc = "top", label.x = NULL,
  label.y = 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.

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.

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.

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 glance is returned as is in the data object. If you do not know what names to expect for the variables returned, use broom::glance() and names() or print() to find out.

Warning!

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

Examples

# NOT RUN {
library(ggplot2)
# Correlation example
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)
my.df <- data.frame(x, y)
# estimated correlation is in column "estimate"!
broom::glance(cor.test(x, y))
ggplot(my.df, aes(x, y)) +
  geom_point() +
  stat_fit_glance(geom = "text",
                  method = "cor.test",
                  method.args = list(x = x, y = y, method = "spearman"),
                  aes(label = sprintf('r[s]~"="~%.2f~~italic(P)~"="~%.2f',
                      stat(estimate), stat(p.value))),
                  parse = TRUE)

# }