Solved – Understanding and Coding Random Intercept Correlation (lmer)

It represents the relationship between the intercepts and coefficients. Since both are allowed to vary by group—they are "random" effects—each group has its own intercept and coefficient. That Corr = .07 means that there is a small, positive relationship between intercepts and slopes. Whether or not this is meaningful depends on the meaning of an intercept.

There are a few good examples that I find helpful. Imagine we are trying out a new 10-week class for learning multilevel modeling. People take a test on the material before they start (week 0) and then after each week 1 – 10. Imagine, for simplicity's sake, we only do it on four people. We do the study, and we find out something troubling: The class is successful in teaching people… but it is not a beginner's class! The only people that improve are the people that had good scores at the beginning (week = 0, making it the intercept). This would be a positive relationship between slope and intercept: As someone's score at week zero (the y-intercept) increases, their slope also increases. This could look like:

set.seed(1839) x <- 0:10 y1 <- 5 + 0 * x + rnorm(11, 0, 1) y2 <- 10 + 2 * x + rnorm(11, 0, 5) y3 <- 20 + 4 * x + rnorm(11, 0, 5) y4 <- 30 + 6 * x + rnorm(11, 0, 5)  dat <- data.frame(week = rep(x, 4), test_score = c(y1, y2, y3, y4),            person = c(rep("a", 11), rep("b", 11),                       rep("c", 11), rep("d", 11)))  library(ggplot2) ggplot(dat, aes(x = week, y = test_score, color = person)) +   geom_point() +   geom_smooth(method = "lm", se = FALSE) 

On the other hand, we could look at a negative relationship between the two. Let's say we do the same class, except this time it is too easy: People come into the class already with the highest scores possible. But people who came in not knowing much? They get brought up to speed to the people who already knew stuff. This means that as the scores at the beginning (week = 0, the y-intercept) get higher, the slopes get weaker. This could look like:

set.seed(1839) x <- 0:10 y1 <- 90 - 0 * x + rnorm(11, 0, 5) y2 <- 60 + 2 * x + rnorm(11, 0, 5) y3 <- 40 + 4 * x + rnorm(11, 0, 5) y4 <- 20 + 6 * x + rnorm(11, 0, 5)  dat <- data.frame(week = rep(x, 4), test_score = c(y1, y2, y3, y4),            person = c(rep("a", 11), rep("b", 11),                       rep("c", 11), rep("d", 11)))  library(ggplot2) ggplot(dat, aes(x = week, y = test_score, color = person)) +   geom_point() +   geom_smooth(method = "lm", se = FALSE) 

It is important to remember that this effect is dependent on how you scale your variables. If you mean-center your predictor, the correlation between intercept and slope is the relationship between the two at the mean of the predictor (since mean-centering makes the y-intercept the mean). Similarly, if you measure people at 40 years-old, 50 years-old, and 60 years-old, then the y-intercept is essentially meaningless, since it is far outside the bounds of what you measured (0 years-old, being their birth).

If you want to test the significance of this relationship, you can compare models including and excluding that correlation. Removing the correlation can be done by including a || in the lmer random effects formula. More details here: http://rpsychologist.com/r-guide-longitudinal-lme-lmer#conditional-growth-model-dropping-intercept-slope-covariance