Mixed Effects

{ggplot2}

• Example 1: Fixed Effect + Varying Intercepts
  

  usl$pred_m1 <- predict(m1)
  usl %>%
    filter(pidp %in% 1:5) %>% # select just five individuals
    ggplot(aes(wave, pred_m1, color = pidp)) +
    geom_point(aes(wave, logincome)) + # points for observed log income
    geom_smooth(method = lm, se = FALSE) + # linear line showing wave0 slope
    theme_bw() +
    labs(x = "Wave", y = "Logincome") +
    theme(legend.position = "none")

  • Model from Mixed Effects, General >> Examples >> Example 1
  • wave was indexed to 0 for the model but now wave starts at 1. He might’ve reverted wave to have a starting value of 1 for graphing purposes
  • Lines show the small, positive, fixed effect slope for wave0
  • Parallel lines means we assume the change in log income over time is the same for all the individuals
    • i.e. We assume there is no between-case variation in the rate of change.

• Example 2: Varying Slopes and Intercepts
  

  usl$pred_m2 <- predict(m2) 
  usl %>%  
    filter(pidp %in% 1:5) %>% # select just two individuals 
    ggplot(aes(wave, pred_m2, color = pidp)) + 
    geom_point(aes(wave, logincome)) + # points for observed logincome 
    geom_smooth(method = lm, se = FALSE) + # linear line based on prediction 
    theme_bw() + # nice theme 
    labs(x = "Wave", y = "Logincome") + # nice labels 
    theme(legend.position = "none")

  • Model from Mixed Effects, General >> Examples >> Example 1
  • Different slopes for each person

{ggeffects}

• Example 1: {ggeffects} Error Bar Plot
  

  library(ggeffects); library(ggplot2)
  # create plot dataframe
  # Has 95% CIs for fixed effects and lists random effects
  plot_data <- 
    ggpredict(
      fit, 
      terms = c("Season")
    )
  
  #create plot
  plot_data |> 
    #reorder factor levels for plotting
    mutate(
      x = ordered(
            x, 
            levels = c("Preseason", "Inseason", "Postseason")
          )
    ) |> 
    #use plot function with ggpredict objects
    plot() +
    #add ggplot2 as needed
    theme_blank() + 
    ylim(c(3000,7000)) + 
    ggtitle("Session Distance by Season Phase")

  • Description:
    • Outcome: Distance
    • Fixed Effect: Season
    • Random Effect: Athlete

{merTools}

• Example 1: Visualize Random Effects]
  

  library(merTools)
  REsim(mod_stress_nested) |> 
    plotREsim() +
    theme_notebook()

  • Model from Mixed Effects, General >> Examples >> Example 3
  • The visual makes it apparent there’s much more variance in the random effects for ward than there is for hospital.