Slippery slopes: Random Intercept and Random Slope Model
Now we can move on to fit the random intercept and random slope model to the rat growth data. Fitting a random intercept and random slope model allows the linear regression fits for each individual to differ in intercept but also in slope. This way it is possible to account for the individual differences in the rats' growth profiles, but also the effect of time.
Este ejercicio forma parte del curso
Helsinki Open Data Science
Instrucciones del ejercicio
- Fit the random intercept and slope model with
TimeandIDas the random effects - Print the summary of the model
- Compute the analysis of variance tables of the models
RATS_refandRATS_ref1 - Pay attention to the chi-squared statistics and p-value of the likelihood ratio test between
RATS_ref1andRATS_ref. The lower the value the better the fit against the comparison model.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# dplyr, tidyr, lme4, ggplot2, RATS and RATSL are available
# create a random intercept and random slope model
RATS_ref1 <- lmer(Weight ~ Time + Group + (Time | ID), data = RATSL, REML = FALSE)
# print a summary of the model
# perform an ANOVA test on the two models
anova(RATS_ref1, RATS_ref)