ggpmisc (version 0.3.9)

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 'tidy() methods from packages 'broom', 'broom.mixed', or other sources. 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),
  tidy.args = list(),
  tb.type = "fit.summary",
  tb.vars = NULL,
  tb.params = NULL,
  digits = 3,
  p.digits = digits,
  label.x = "center",
  label.y = "top",
  label.x.npc = NULL,
  label.y.npc = NULL,
  position = "identity",
  table.theme = NULL,
  table.rownames = FALSE,
  table.colnames = TRUE,
  table.hjust = 1,
  parse = FALSE,
  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, tidy.args

lists of arguments to pass to method and to tidy().

tb.type

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

tb.vars, tb.params

character or numeric vectors, optionally named, used to select and/or rename the columns or the parameters in the table returned.

digits

integer indicating the number of significant digits to be used for all numeric values in the table.

p.digits

integer indicating the number of decimal places to round p-values to, with those rounded to zero displayed as the next larger possible value preceded by "<". If p.digits is outside the range 1..22 no rounding takes place.

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

table.theme

NULL, list or function A gridExtra ttheme defintion, or a constructor for a ttheme or NULL for default.

table.rownames, table.colnames

logical flag to enable or disabling printing of row names and column names.

table.hjust

numeric Horizontal justification for the core and column headings of the table.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

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 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.

See Also

broom and broom.mixed for details on how the tidying of the result of model fits is done. See geom_table for details on how inset tables respond to mapped aesthetics and table themes. For details on predefined table themes see ttheme_gtdefault.

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

Examples

# NOT RUN {
library(broom)

# 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 fit summary, by default
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb() +
  expand_limits(y = 70)

# Linear regression fit summary, by default
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb(digits = 2, p.digits = 4) +
  expand_limits(y = 70)

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

# Linear regression ANOVA table
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb(tb.type = "fit.anova") +
  expand_limits(y = 70)

# Linear regression fit coeficients
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb(tb.type = "fit.coefs") +
  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)

# Polynomial regression with renamed parameters
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb(method.args = list(formula = y ~ poly(x, 2)),
              tb.params = c("x^0" = 1, "x^1" = 2, "x^2" = 3),
              parse = TRUE) +
  expand_limits(y = 70)

# Polynomial regression with renamed parameters and columns
# using numeric indexes
ggplot(my.df, aes(covariate, x)) +
  geom_point() +
  stat_fit_tb(method.args = list(formula = y ~ poly(x, 2)),
              tb.params = c("x^0" = 1, "x^1" = 2, "x^2" = 3),
              tb.vars = c("Term" = 1, "Estimate" = 2, "S.E." = 3,
                          "italic(F)-value" = 4, "italic(P)-value" = 5),
              parse = TRUE) +
  expand_limits(y = 70)

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

# ANOVA table
ggplot(my.df, aes(group, x)) +
  geom_point() +
  stat_fit_tb(tb.type = "fit.anova") +
  expand_limits(y = 70)

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

# ANOVA table with renamed and selected columns
# using column names with partial matching
ggplot(my.df, aes(group, x)) +
  geom_point() +
  stat_fit_tb(tb.type = "fit.anova",
              tb.vars = c(Effect = "term", "df", "italic(F)" = "stat",
                          "italic(P)" = "p"),
              parse = TRUE)

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

# 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)

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

# }