ggpmisc (version 0.3.1)

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 package 'broom'. To add the summary in tabular form use stat_fit_tb. 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),
  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.

method.args

list of arguments to pass to method.

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.

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.

Examples

# NOT RUN {
# Regression example
my.df <-
  data.frame(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))

ggplot(my.df, aes(X, Y)) +
  geom_point() +
  stat_fit_tidy(method = "lm",
                method.args = list(formula = y ~ x),
                mapping = aes(label = sprintf("Slope = %.3g\np-value = %.3g",
                                              stat(x_estimate), stat(x_p.value))))

# }