inst/doc/art-contrasts.R

In ARTool: Aligned Rank Transform

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(  #default code chunk options
  fig.width = 6,
  fig.height = 4
)
pander::panderOptions("table.split.table", Inf)     #don't split wide tables in output
pander::panderOptions("table.style", "rmarkdown")   #table style that's supported by github

## ----message=FALSE, warning=FALSE---------------------------------------------
library(dplyr)      #data_frame, %>%, filter, summarise, group_by
library(emmeans)    #emmeans, contrast
library(phia)       #testInteractions
library(tidyr)      #spread
library(ARTool)     #art, artlm
library(ggplot2)    #ggplot, stat_..., geom_..., etc

## -----------------------------------------------------------------------------
n_per_group = 150
df = tibble(
  X1 = factor(c(rep("A", n_per_group), rep("B", n_per_group))),
  X2 = factor(rep(c("C","D","E"), n_per_group * 2/3)),
  Y = rnorm(
    n_per_group * 2, 
    (X1 == "B")
    + 2* (X2 == "D")
    + 2 * (X1 == "B" & X2 == "D")
    - 2 * (X1 == "A" & X2 == "D")
    + 2 * (X2 == "E")
  ) 
)

## -----------------------------------------------------------------------------
data(InteractionTestData, package = "ARTool")
df = InteractionTestData    #save some typing

## ----interaction_plot, fig.cap=""---------------------------------------------
# variant of the Dark2 colorbrewer scale with specific name mappings (so 
# we can keep color -> name mapping consistent throughout this document)
palette = c("#1b9e77", "#d95f02", "#7570b3")
names(palette) = c("C", "D", "E")

df %>%
  ggplot(aes(x = X1, y = Y, color = X2)) +
  geom_violin(trim = FALSE, adjust = 1.5) +
  geom_point(pch = "-", size = 4) +
  stat_summary(fun = mean, geom = "point", size = 4) +
  stat_summary(aes(group = X2), fun = mean, geom = "line", size = 1) +
  stat_summary(aes(x = 1.5, group = NA), fun = mean, geom = "point", size = 9, pch = "+") +
  scale_y_continuous(breaks = seq(-6, 10, by = 2), minor_breaks = -6:10) +
  scale_color_manual(guide = FALSE, values = palette) +
  coord_cartesian(ylim = c(-6, 10)) +
  facet_grid(. ~ X2)

## -----------------------------------------------------------------------------
m.linear = lm(Y ~ X1*X2, data = df)
anova(m.linear)

## -----------------------------------------------------------------------------
m.art = art(Y ~ X1*X2, data = df)
anova(m.art)

## ---- message=FALSE-----------------------------------------------------------
contrast(emmeans(m.linear, ~ X1), method = "pairwise")
contrast(emmeans(m.linear, ~ X2), method = "pairwise")

## ---- message=FALSE-----------------------------------------------------------
# this works for single factors, though it is better (more general) to use
# artlm.con() or art.con() (see below)
contrast(emmeans(artlm(m.art, "X1"), ~ X1), method = "pairwise")
contrast(emmeans(artlm(m.art, "X2"), ~ X2), method = "pairwise")

## ---- message=FALSE-----------------------------------------------------------
contrast(emmeans(artlm.con(m.art, "X1"), ~ X1), method = "pairwise")
contrast(emmeans(artlm.con(m.art, "X2"), ~ X2), method = "pairwise")

## ---- message=FALSE-----------------------------------------------------------
art.con(m.art, "X1")
art.con(m.art, "X2")

## -----------------------------------------------------------------------------
contrast(emmeans(m.linear, ~ X1:X2), method = "pairwise")

## -----------------------------------------------------------------------------
#DO NOT DO THIS!
contrast(emmeans(artlm(m.art, "X1:X2"), ~ X1:X2), method = "pairwise")

## ----interaction_plot_AC_AD, fig.cap="", fig.width=3--------------------------
df %>%
  filter(X1 == "A", X2 %in% c("C", "D")) %>%
  ggplot(aes(x = X1:X2, y = Y, color = X2)) +
  geom_violin(trim = FALSE, adjust = 1.5) +
  geom_point(pch = "-", size = 4) +
  stat_summary(fun = mean, geom = "point", size = 4) +
  scale_y_continuous(breaks = seq(-6, 10, by = 2), minor_breaks = -6:10) +
  scale_color_manual(guide = FALSE, values = palette) +
  coord_cartesian(ylim=c(-6,10)) 

## ----message = FALSE----------------------------------------------------------
art.con(m.art, "X1:X2")

## ----message = FALSE----------------------------------------------------------
art.con(m.art, ~ X1*X2)

## ----message = FALSE----------------------------------------------------------
contrast(emmeans(artlm.con(m.art, "X1:X2"), ~ X1X2), method = "pairwise")

## ---- interaction_plot_C_D, fig.cap=""----------------------------------------
plot_interaction_for_X2_levels = function(...) {
  x2_levels = c(...)
  df. = filter(df, X2 %in% x2_levels)
  
  X1_in_X2 = df. %>%
    group_by(X1, X2) %>%
    summarise(Y = mean(Y), .groups = "drop") %>%
    spread(X1, Y)
  
  print(
    ggplot(df., aes(x = X1, y = Y, color = X2)) +  
      geom_violin(trim = FALSE, adjust = 1.5) + 
      geom_point(pch = "-", size = 4) +
      stat_summary(fun = mean, geom = "point", size = 4) + 
      stat_summary(aes(group = X2), fun = mean, geom = "line", size = 1, linetype = "dashed") +
      geom_errorbar(
        aes(x = 2.2, ymin = A, ymax = B, y = NULL),  
        data = X1_in_X2, width = .19, size = 0.8, color = "black"
      ) +
      geom_text(
        aes(x = 2.35, y = (A + B)/2, label = paste("A - B |", X2)), 
        data = X1_in_X2, hjust = 0, size = 5, color = "black"
      ) +
      scale_y_continuous(breaks = seq(-6, 10, by = 2), minor_breaks = -6:10) +
      scale_color_manual(guide = FALSE, values = palette[x2_levels]) +
      coord_cartesian(xlim = c(0, 3.5), ylim = c(-6,10)) +
      facet_grid(. ~ X2)
  )
}
plot_interaction_for_X2_levels("C", "D")

## -----------------------------------------------------------------------------
contrast(emmeans(m.linear, ~ X1:X2), method = "pairwise", interaction = TRUE)

## ----interaction_plot_C_E, fig.cap=""-----------------------------------------
plot_interaction_for_X2_levels("C", "E")

## ---- interaction_plot_D_E, fig.cap=""----------------------------------------
plot_interaction_for_X2_levels("D", "E")

## -----------------------------------------------------------------------------
art.con(m.art, "X1:X2", interaction = TRUE)

## -----------------------------------------------------------------------------
contrast(emmeans(artlm(m.art, "X1:X2"), ~ X1:X2), method = "pairwise", interaction = TRUE)

## -----------------------------------------------------------------------------
testInteractions(artlm(m.art, "X1:X2"), pairwise = c("X1","X2"))