Time to interact: Random Intercept and Random Slope Model with interaction

Finally, we can fit a random intercept and slope model that allows for a group × time interaction.

This exercise is part of the course

Helsinki Open Data Science

Exercise instructions

Write the same model as in the previous exercise but add Time * Group interaction.
Print out the summary of the model
Compute the analysis of variance tables of the models RATS_ref2 and RATS_ref1
Again pay attention to the likelihood ratio test chi-squared value and the according p-value. The lower the value the better the fit against the comparison model.
Draw the plot of observed values of RATSL (this is the same plot drawn earlier)
Create a vector of the fitted values of the model using the function fitted()
Use for example mutate() to add the vector Fitted as a new column to RATSL
Draw the plot of fitted values of RATSL

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# dplyr, tidyr, lme4, ggplot2, RATS and RATSL are available

# create a random intercept and random slope model with the interaction
RATS_ref2 <- "Write the model here"

# print a summary of the model


# perform an ANOVA test on the two models
anova(RATS_ref2, RATS_ref1)

# draw the plot of RATSL with the observed Weight values
ggplot(RATSL, aes(x = Time, y = Weight, group = ID)) +
  geom_line(aes(linetype = Group)) +
  scale_x_continuous(name = "Time (days)", breaks = seq(0, 60, 20)) +
  scale_y_continuous(name = "Observed weight (grams)") +
  theme(legend.position = "top")

# Create a vector of the fitted values
Fitted <- "Change me!"

# Create a new column fitted to RATSL


# draw the plot of RATSL with the Fitted values of weight
ggplot(RATSL, aes(x = Time, y = "Change me!", group = ID)) +
  geom_line(aes(linetype = Group)) +
  scale_x_continuous(name = "Time (days)", breaks = seq(0, 60, 20)) +
  scale_y_continuous(name = "Fitted weight (grams)") +
  theme(legend.position = "top")

Edit and Run Code