ggpmisc (version 0.3.9)

stat_poly_eq: Equation, p-value, R^2, AIC or BIC of fitted polynomial

Description

stat_poly_eq fits a polynomial and generates several labels including the equation, p-value, coefficient of determination (R^2), 'AIC' and 'BIC'.

Usage

stat_poly_eq(
  mapping = NULL,
  data = NULL,
  geom = "text_npc",
  position = "identity",
  ...,
  formula = NULL,
  eq.with.lhs = TRUE,
  eq.x.rhs = NULL,
  coef.digits = 3,
  rr.digits = 2,
  f.digits = 3,
  p.digits = 3,
  label.x = "left",
  label.y = "top",
  label.x.npc = NULL,
  label.y.npc = NULL,
  hstep = 0,
  vstep = NULL,
  output.type = "expression",
  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

position

The position adjustment to use for overlapping points on this layer

...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

formula

a formula object. Using aesthetic names instead of original variable names.

eq.with.lhs

If character the string is pasted to the front of the equation label before parsing or a logical (see note).

eq.x.rhs

character this string will be used as replacement for "x" in the model equation when generating the label before parsing it.

coef.digits, f.digits

integer Number of significant digits to use for the fitted coefficients and F-value.

rr.digits, p.digits

integer Number of digits after the decimal point to use for R^2 and P-value in labels.

label.x, label.y

numeric with range 0..1 "normalized parent coordinates" (npc units) or character if using geom_text_npc() or geom_label_npc(). If using geom_text() or geom_label() numeric in native data units. If too short they will be recycled.

label.x.npc, label.y.npc

numeric with range 0..1 (npc units) DEPRECATED, use label.x and label.y instead; together with a geom using npcx and npcy aesthetics.

hstep, vstep

numeric in npc units, the horizontal and vertical step used between labels for different groups.

output.type

character One of "expression", "LaTeX", "text", "markdown" or "numeric".

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.

Aesthetics

stat_poly_eq understands x and y, to be referenced in the formula and weight passed as argument to parameter weights of lm(). All three must be mapped to numeric variables. In addition, the aesthetics undertood by the geom used ("text" by default) are understood and grouping respected.

Computed variables

If output.type different from "numeric" the returned tibble contains columns:

x,npcx

x position

y,npcy

y position

coef.ls, r.squared, adj.r.squared, AIC, BIC

as numric values extracted from fit object

eq.label

equation for the fitted polynomial as a character string to be parsed

rr.label

\(R^2\) of the fitted model as a character string to be parsed

adj.rr.label

Adjusted \(R^2\) of the fitted model as a character string to be parsed

f.value.label

F value and degrees of freedom for the fitted model as a whole.

p.value..label

P-value for the F-value above.

AIC.label

AIC for the fitted model.

BIC.label

BIC for the fitted model.

hjust, vjust

Set to "inward" to override the default of the "text" geom.

If output.type is "numeric" the returned tibble contains columns:

x,npcx

x position

y,npcy

y position

coef.ls

list containing the "coefficients" matrix from the summary of the fit object

r.squared, adj.r.squared, f.value, f.df1, f.df2, p.value, AIC, BIC

numeric values extracted or computed from fit object

hjust, vjust

Set to "inward" to override the default of the "text" geom.

To explore the computed values returned for a given input we suggest the use of geom_debug as shown in the example below.

Parsing may be required

if using the computed labels with output.type = "expression", then parse = TRUE is needed, while if using output.type = "LaTeX" parse = FALSE is needed.

Details

This stat can be used to automatically annotate a plot with R^2, adjusted R^2 or the fitted model equation. It supports only linear models fitted with function lm(). The R^2 and adjusted R^2 annotations can be used with any linear model formula. The fitted equation label is correctly generated for polynomials or quasi-polynomials through the origin. Model formulas can use poly() or be defined algebraically with terms of powers of increasing magnitude with no missing intermediate terms, except possibly for the intercept indicated by "- 1" or "-1" in the formula. The validity of the formula is not checked in the current implementation, and for this reason the default aesthetics sets R^2 as label for the annotation. This stat only generates labels, the predicted values need to be separately added to the plot, so to make sure that the same model formula is used in all steps it is best to save the formula as an object and supply this object as argument to the different statistics.

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. stat_poly_eq() mimics how stat_smooth() works, except that only polynomials can be fitted. In other words, it respects the grammar of graphics. This helps ensure that the model is fitted to the same data as plotted in other layers.

References

Written as an answer to a question at Stackoverflow. https://stackoverflow.com/questions/7549694/adding-regression-line-equation-and-r2-on-graph

See Also

This stat_poly_eq statistic can return ready formatted labels depending on the argument passed to output.type. This is possible because only polynomial models are supported. For other types of models, statistics stat_fit_glance, stat_fit_tidy and stat_fit_glance should be used instead and the code for construction of character strings from numeric values and their mapping to aesthetic label needs to be explicitly supplied in the call.

Other statistics for linear model fits: stat_fit_deviations(), stat_fit_residuals()

Examples

# NOT RUN {
# generate artificial data
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x = x, y = y,
                      group = c("A", "B"),
                      y2 = y * c(0.5,2),
                      w = sqrt(x))

# give a name to a formula
formula <- y ~ poly(x, 3, raw = TRUE)

# no weights
ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, parse = TRUE)

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, parse = TRUE,
               label.y = "bottom", label.x = "right")

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, parse = TRUE,
               label.y = 0.1, label.x = 0.9)

# using weights
ggplot(my.data, aes(x, y, weight = w)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, parse = TRUE)

# no weights, digits for R square
ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, rr.digits = 4, parse = TRUE)

# user specified label
ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(aes(label =  paste(stat(eq.label),
                                  stat(adj.rr.label), sep = "*\", \"*")),
               formula = formula, parse = TRUE)

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(aes(label =  paste(stat(f.value.label),
                                  stat(p.value.label), sep = "*\", \"*")),
               formula = formula, parse = TRUE)

# user specified label and digits
ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(aes(label =  paste(stat(eq.label),
                                  stat(adj.rr.label), sep = "*\", \"*")),
               formula = formula, rr.digits = 3, coef.digits = 4,
               parse = TRUE)

# geom = "text"
ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(geom = "text", label.x = 100, label.y = 0, hjust = 1,
               formula = formula, parse = TRUE)

# using numeric values
# Here we use column "Estimate" from the matrix.
# Other available columns are "Std. Error", "t value" and "Pr(>|t|)".
my.format <-
  "b[0]~`=`~%.3g*\", \"*b[1]~`=`~%.3g*\", \"*b[2]~`=`~%.3g*\", \"*b[3]~`=`~%.3g"
ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula,
               output.type = "numeric",
               parse = TRUE,
               mapping =
                aes(label = sprintf(my.format,
                                    stat(coef.ls)[[1]][[1, "Estimate"]],
                                    stat(coef.ls)[[1]][[2, "Estimate"]],
                                    stat(coef.ls)[[1]][[3, "Estimate"]],
                                    stat(coef.ls)[[1]][[4, "Estimate"]])
                                    )
                   )

# Examples using geom_debug() to show computed values
#
# This provides a quick way of finding out which variables are available for
# use in mapping of aesthetics when using other geoms as in the examples
# above.

library(gginnards)

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, geom = "debug")

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(aes(label = stat(eq.label)),
               formula = formula, geom = "debug",
               output.type = "markdown")

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, geom = "debug", output.type = "text")

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, geom = "debug", output.type = "numeric")

# show the content of a list column
ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(formula = formula, geom = "debug", output.type = "numeric",
               summary.fun = function(x) {x[["coef.ls"]][[1]]})

# }