visualisation_recipe.estimate_predicted: Automated plotting for 'modelbased' objects

In easystats/estimate: Estimation of Model-Based Predictions, Contrasts and Means

Automated plotting for 'modelbased' objects

Description

Most 'modelbased' objects can be visualized using the plot() function, which internally calls the visualisation_recipe() function. See the examples below for more information and examples on how to create and customize plots.

Usage

## S3 method for class 'estimate_predicted'
visualisation_recipe(
  x,
  show_data = FALSE,
  point = NULL,
  line = NULL,
  pointrange = NULL,
  ribbon = NULL,
  facet = NULL,
  grid = NULL,
  join_dots = NULL,
  numeric_as_discrete = NULL,
  ...
)

## S3 method for class 'estimate_slopes'
visualisation_recipe(
  x,
  line = NULL,
  pointrange = NULL,
  ribbon = NULL,
  facet = NULL,
  grid = NULL,
  ...
)

## S3 method for class 'estimate_grouplevel'
visualisation_recipe(
  x,
  line = NULL,
  pointrange = NULL,
  ribbon = NULL,
  facet = NULL,
  grid = NULL,
  ...
)

Arguments

x	A modelbased object.
show_data	Logical, if TRUE, display the "raw" data as a background to the model-based estimation.
point, line, pointrange, ribbon, facet, grid	Additional aesthetics and parameters for the geoms (see customization example).
join_dots	Logical, if TRUE (default) and for categorical focal terms in by, dots (estimates) are connected by lines, i.e. plots will be a combination of dots with error bars and connecting lines. If FALSE, only dots and error bars are shown. It is possible to set a global default value using options(), e.g. options(modelbased_join_dots = FALSE).
numeric_as_discrete	Maximum number of unique values in a numeric predictor to treat that predictor as discrete. Defaults to 8. Numeric predictors are usually mapped to a continuous color scale, unless they have only few unique values. In the latter case, numeric predictors are assumed to represent "categories", e.g. when only the mean value and +/- 1 standard deviation around the mean are chosen as representative values for that predictor. Use FALSE to always use continuous color scales for numeric predictors. It is possible to set a global default value using options(), e.g. options(modelbased_numeric_as_discrete = 10).
...	Not used.

Details

The plotting works by mapping any predictors from the by argument to the x-axis, colors, alpha (transparency) and facets. Thus, the appearance of the plot depends on the order of the variables that you specify in the by argument. For instance, the plots corresponding to estimate_relation(model, by=c("Species", "Sepal.Length")) and estimate_relation(model, by=c("Sepal.Length", "Species")) will look different.

The automated plotting is primarily meant for convenient visual checks, but for publication-ready figures, we recommend re-creating the figures using the ggplot2 package directly.

There are two options to remove the confidence bands or errors bars from the plot. To remove error bars, simply set the pointrange geom to point, e.g. plot(..., pointrange = list(geom = "point")). To remove the confidence bands from line geoms, use ribbon = "none".

Global Options to Customize Plots

Some arguments for plot() can get global defaults using options():

• modelbased_join_dots: ⁠options(modelbased_join_dots = <logical>)⁠ will set a default value for the join_dots.

• modelbased_numeric_as_discrete: ⁠options(modelbased_numeric_as_discrete = <number>)⁠ will set a default value for the modelbased_numeric_as_discrete argument. Can also be FALSE.

Examples

library(ggplot2)
library(see)
# ==============================================
# estimate_relation, estimate_expectation, ...
# ==============================================
# Simple Model ---------------
x <- estimate_relation(lm(mpg ~ wt, data = mtcars))
layers <- visualisation_recipe(x)
layers
plot(layers)

# visualization_recipe() is called implicitly when you call plot()
plot(estimate_relation(lm(mpg ~ qsec, data = mtcars)))

## Not run: 
# It can be used in a pipe workflow
lm(mpg ~ qsec, data = mtcars) |>
  estimate_relation(ci = c(0.5, 0.8, 0.9)) |>
  plot()

# Customize aesthetics ----------

plot(x,
  point = list(color = "red", alpha = 0.6, size = 3),
  line = list(color = "blue", size = 3),
  ribbon = list(fill = "green", alpha = 0.7)
) +
  theme_minimal() +
  labs(title = "Relationship between MPG and WT")

# Customize raw data -------------

plot(x, point = list(geom = "density_2d_filled"), line = list(color = "white")) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(legend.position = "none")

# Single predictors examples -----------

plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris)))

# 2-ways interaction ------------

# Numeric * numeric
x <- estimate_relation(lm(mpg ~ wt * qsec, data = mtcars))
plot(x)

# Numeric * factor
x <- estimate_relation(lm(Sepal.Width ~ Sepal.Length * Species, data = iris))
plot(x)

# ==============================================
# estimate_means
# ==============================================
# Simple Model ---------------
x <- estimate_means(lm(Sepal.Width ~ Species, data = iris), by = "Species")
layers <- visualisation_recipe(x)
layers
plot(layers)

# Customize aesthetics
layers <- visualisation_recipe(x,
  point = list(width = 0.03, color = "red"),
  pointrange = list(size = 2, linewidth = 2),
  line = list(linetype = "dashed", color = "blue")
)
plot(layers)

# Two levels ---------------
data <- mtcars
data$cyl <- as.factor(data$cyl)

model <- lm(mpg ~ cyl * wt, data = data)

x <- estimate_means(model, by = c("cyl", "wt"))
plot(x)


# GLMs ---------------------
data <- data.frame(vs = mtcars$vs, cyl = as.factor(mtcars$cyl))
x <- estimate_means(glm(vs ~ cyl, data = data, family = "binomial"), by = c("cyl"))
plot(x)

## End(Not run)


# ==============================================
# estimate_slopes
# ==============================================
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
x <- estimate_slopes(model, trend = "Petal.Length", by = "Species")

layers <- visualisation_recipe(x)
layers
plot(layers)

## Not run: 
# Customize aesthetics and add horizontal line and theme
layers <- visualisation_recipe(x, pointrange = list(size = 2, linewidth = 2))
plot(layers) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
  theme_minimal() +
  labs(y = "Effect of Petal.Length", title = "Marginal Effects")

model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris)
x <- estimate_slopes(model, trend = "Sepal.Width", by = "Sepal.Width", length = 20)
plot(visualisation_recipe(x))

model <- lm(Petal.Length ~ Species * poly(Sepal.Width, 3), data = iris)
x <- estimate_slopes(model, trend = "Sepal.Width", by = c("Sepal.Width", "Species"))
plot(visualisation_recipe(x))

## End(Not run)


# ==============================================
# estimate_grouplevel
# ==============================================
## Not run: 
data <- lme4::sleepstudy
data <- rbind(data, data)
data$Newfactor <- rep(c("A", "B", "C", "D"))

# 1 random intercept
model <- lme4::lmer(Reaction ~ Days + (1 | Subject), data = data)
x <- estimate_grouplevel(model)
layers <- visualisation_recipe(x)
layers
plot(layers)

# 2 random intercepts
model <- lme4::lmer(Reaction ~ Days + (1 | Subject) + (1 | Newfactor), data = data)
x <- estimate_grouplevel(model)
plot(x) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_minimal()
# Note: we need to use hline instead of vline because the axes is flipped

model <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Newfactor), data = data)
x <- estimate_grouplevel(model)
plot(x)

## End(Not run)

easystats/estimate documentation built on April 5, 2025, 1:36 p.m.