Session Ready
Exercise

Random-effect slopes

In the previous exercise, you saw how to code random-effect intercepts. During this exercise, you will see how to code random-effect slopes. With lme4 syntax, lmer() uses (countinuousPredictor|randomEffectGroup) for a random effect slope. When lme4 estimates a random-effect slope, it also estimates a random-effect intercept.

After fitting this model, you will see how to extract and plot the fitted model. ggplot2 can plot many models using geom_smooth() or stat_smooth(), but not all models. One trick to plot models not included with ggplot2 is to use the predict() function to extract predicted values for a fit model. These points can then be plotted with ggplot2 using geom_line().

Instructions 1/2
undefined XP
  • 1
  • 2

  • Use the lmer function from the lme4 package to fit a random-effects slope model. Using the data.frame multIntDemo to examine how response can be predicted by a random slope using group and x.

  • Examine both the default summary() output and the tidy output. Notice how it differs from a normal linear model.

    Loading