modelbased (version 0.7.0)

visualisation_recipe: Prepare objects for visualisation

Description

This function prepares some objects for visualisation by returning a list of layers with data and geoms that can be easily plotted using ggplot2.

Usage

visualisation_recipe(x, ...)
# S3 method for estimate_grouplevel
visualisation_recipe(
  x,
  hline = NULL,
  pointrange = NULL,
  facet_wrap = NULL,
  labs = NULL,
  ...
)
# S3 method for estimate_means
visualisation_recipe(
  x,
  show_data = "jitter",
  point = NULL,
  jitter = point,
  boxplot = NULL,
  violin = NULL,
  line = NULL,
  pointrange = NULL,
  labs = NULL,
  ...
)
# S3 method for estimate_predicted
visualisation_recipe(
  x,
  show_data = "points",
  point = NULL,
  density_2d = NULL,
  line = NULL,
  ribbon = NULL,
  labs = NULL,
  ...
)

Arguments

x

An easystats object.

...

Other arguments passed to other functions.

show_data

Display the "raw" data as a background to the model-based estimation. Can be set to "none" to remove it. When input is the result of estimate_means, show_data can be "points" (the jittered observation points), "boxplot", "violin" a combination of them (see examples). When input is the result of estimate_response or estimate_expectation, show_data can be "points" (the points of the original data corresponding to the x and y axes), "density_2d", "density_2d_filled", "density_2d_polygon" or "density_2d_raster".

point, jitter, boxplot, violin, pointrange, density_2d, line, hline, ribbon, labs, facet_wrap

Additional aesthetics and parameters for the geoms (see customization example).

Examples

# NOT RUN {
# ==============================================
# estimate_grouplevel
# ==============================================
if (require("see") && require("lme4")) {
  data <- lme4::sleepstudy
  data <- rbind(data, data)
  data$Newfactor <- rep(c("A", "B", "C", "D"))

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

  # 2 random intercepts
  model <- lmer(Reaction ~ Days + (1 | Subject) + (1 | Newfactor), data = data)
  x <- estimate_grouplevel(model)
  plot(visualisation_recipe(x))


  model <- lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Newfactor), data = data)
  x <- estimate_grouplevel(model)
  plot(visualisation_recipe(x))
}
# ==============================================
# estimate_means
# ==============================================
if (require("ggplot2")) {

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

  # Customize aesthetics
  layers <- visualisation_recipe(x,
    jitter = list(width = 0.03, color = "red"),
    line = list(linetype = "dashed")
  )
  plot(layers)

  # Customize raw data
  plot(visualisation_recipe(x, show_data = c("violin", "boxplot", "jitter")))

  # Two levels ---------------
  data <- mtcars
  data$cyl <- as.factor(data$cyl)
  data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars)))

  model <- lm(mpg ~ new_factor * cyl * wt, data = data)
  x <- estimate_means(model, levels = c("new_factor", "cyl"))
  plot(visualisation_recipe(x))

  # Modulations --------------
  x <- estimate_means(model, levels = c("new_factor"), modulate = "wt")
  plot(visualisation_recipe(x))

  x <- estimate_means(model, levels = c("new_factor", "cyl"), modulate = "wt")
  plot(visualisation_recipe(x))

  #'   # GLMs ---------------------
  data <- data.frame(vs = mtcars$vs, cyl = as.factor(mtcars$cyl))
  x <- estimate_means(glm(vs ~ cyl, data = data, family = "binomial"))
  plot(visualisation_recipe(x))
}
# ==============================================
# estimate_expectation, estimate_response, ...
# ==============================================
if (require("ggplot2")) {

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

  # Customize aesthetics
  layers <- visualisation_recipe(x,
    point = list(color = "red", alpha = 0.6, size = 3),
    line = list(color = "blue", size = 4),
    ribbon = list(fill = "green", alpha = 0.7),
    labs = list(subtitle = "Oh yeah!")
  )
  layers
  plot(layers)

  # Customize raw data
  plot(visualisation_recipe(x, show_data = "none"))
  plot(visualisation_recipe(x, show_data = c("density_2d", "points")))
  plot(visualisation_recipe(x, show_data = "density_2d_filled"))
  plot(visualisation_recipe(x, show_data = "density_2d_polygon"))
  plot(visualisation_recipe(x, show_data = "density_2d_raster")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0))


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

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

  # Factor * numeric
  x <- estimate_relation(lm(Sepal.Width ~ Species * Sepal.Length, data = iris))
  layers <- visualisation_recipe(x)
  plot(layers)


  # 3-ways interaction ------------
  data <- mtcars
  data$vs <- as.factor(data$vs)
  data$cyl <- as.factor(data$cyl)
  data$new_factor <- as.factor(rep(c("A", "B"), length.out = nrow(mtcars)))

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

  # Numeric * numeric * factor
  x <- estimate_relation(lm(mpg ~ wt * am * vs, data = data))
  layers <- visualisation_recipe(x)
  plot(layers)

  # Numeric * factor * factor
  x <- estimate_relation(lm(mpg ~ wt * cyl * new_factor, data = data))
  layers <- visualisation_recipe(x)
  plot(layers)

  # GLMs ---------------------
  x <- estimate_relation(glm(vs ~ mpg, data = mtcars, family = "binomial"))
  plot(visualisation_recipe(x))
  plot(visualisation_recipe(x, show_data = "jitter", point = list(height = 0.03)))
}
# }