Linear Mixed Effects Models

With linear mixed effects models, we wish to model a linear relationship for data points with inputs of varying type, categorized into subgroups, and associated to a real-valued output.

We demonstrate with an example in Edward. An interactive version with Jupyter notebook is available here.


We use the InstEval data set from the popular lme4 R package (Bates, Mächler, Bolker, & Walker, 2015), located here. It is a data set of instructor evaluation ratings, where the inputs (covariates) include categories such as students and departments, and our response variable of interest is the instructor evaluation rating.

data = pd.read_csv('../../examples/data/insteval.csv')
data['dcodes'] = data['d'].astype('category')
data['deptcodes'] = data['dept'].astype('category')
data['s'] = data['s'] - 1

train = data.sample(frac=0.8)
test = data.drop(train.index)

In the code, we denote:

  • students as s
  • instructors as d
  • departments as dept
  • service as service
n_s = 2972  # number of students
n_d = 1128  # number of instructors
n_dept = 14  # number of departments
n_obs = train.shape[0]  # number of observations


With linear regression, one makes an independence assumption where each data point regresses with a constant slope among each other. In our setting, the observations come from groups which may have varying slopes and intercepts. Thus we’d like to build a model that can capture this behavior (Gelman & Hill, 2006).

For examples of this phenomena:

  • The observations from a single student are not independent of each other. Rather, some students may systematically give low (or high) lecture ratings.
  • The observations from a single teacher are not independent of each other. We expect good teachers to get generally good ratings and bad teachers to get generally bad ratings.
  • The observations from a single department are not independent of each other. One department may generally have dry material and thus be rated lower than others.

Typical linear regression takes the form \[\mathbf{y} = \mathbf{X}\beta + \epsilon,\] where \(\mathbf{X}\) corresponds to fixed effects with coefficients \(\beta\) and \(\epsilon\) corresponds to random noise, \(\epsilon\sim\mathcal{N}(\mathbf{0}, \mathbf{I})\).

In a linear mixed effects model, we add an additional term \(\mathbf{Z}\eta\), where \(\mathbf{Z}\) corresponds to random effects with coefficients \(\eta\). The model takes the form \[\begin{aligned} \eta &\sim \mathcal{N}(\mathbf{0}, \sigma^2 \mathbf{I}), \\ \mathbf{y} &= \mathbf{X}\beta + \mathbf{Z}\eta + \epsilon.\end{aligned}\] Given data, the goal is to infer \(\beta\), \(\eta\), and \(\sigma^2\), where \(\beta\) are model parameters (“fixed effects”), \(\eta\) are latent variables (“random effects”), and \(\sigma^2\) is a variance component parameter.

Because the random effects have mean 0, the data’s mean is captured by \(\mathbf{X}\beta\). The random effects component \(\mathbf{Z}\eta\) captures variations in the data (e.g. Instructor #54 is rated 1.4 points higher than the mean).

A natural question is the difference between fixed and random effects. A fixed effect is an effect that is constant for a given population. A random effect is an effect that varies for a given population (i.e., it may be constant within subpopulations but varies within the overall population). We illustrate below in our example:

  • Select service as the fixed effect. It is a binary covariate corresponding to whether the lecture belongs to the lecturer’s main department. No matter how much additional data we collect, it can only take on the values in \(0\) and \(1\).
  • Select the categorical values of students, teachers, and departments as the random effects. Given more observations from the population of instructor evaluation ratings, we may be looking at new students, teachers, or departments.

In the syntax of R’s lme4 package (Bates et al., 2015), the model can be summarized as

y ~ 1 + (1|students) + (1|instructor) + (1|dept) + service

where 1 denotes an intercept term, (1|x) denotes a random effect for x, and x denotes a fixed effect.

# Set up placeholders for the data inputs.
s_ph = tf.placeholder(tf.int32, [None])
d_ph = tf.placeholder(tf.int32, [None])
dept_ph = tf.placeholder(tf.int32, [None])
service_ph = tf.placeholder(tf.float32, [None])

# Set up fixed effects.
mu = tf.Variable(tf.random_normal([]))
service = tf.Variable(tf.random_normal([]))

sigma_s = tf.sqrt(tf.exp(tf.Variable(tf.random_normal([]))))
sigma_d = tf.sqrt(tf.exp(tf.Variable(tf.random_normal([]))))
sigma_dept = tf.sqrt(tf.exp(tf.Variable(tf.random_normal([]))))

# Set up random effects.
eta_s = Normal(loc=tf.zeros(n_s), scale=sigma_s * tf.ones(n_s))
eta_d = Normal(loc=tf.zeros(n_d), scale=sigma_d * tf.ones(n_d))
eta_dept = Normal(loc=tf.zeros(n_dept), scale=sigma_dept * tf.ones(n_dept))

yhat = tf.gather(eta_s, s_ph) + \
    tf.gather(eta_d, d_ph) + \
    tf.gather(eta_dept, dept_ph) + \
    mu + service * service_ph
y = Normal(loc=yhat, scale=tf.ones(n_obs))


Given data, we aim to infer the model’s fixed and random effects. In this analysis, we use variational inference with the \(\text{KL}(q\|p)\) divergence measure. We specify fully factorized normal approximations for the random effects and pass in all training data for inference. Under the algorithm, the fixed effects will be estimated under a variational EM scheme.

q_eta_s = Normal(
q_eta_d = Normal(
q_eta_dept = Normal(

latent_vars = {
    eta_s: q_eta_s,
    eta_d: q_eta_d,
    eta_dept: q_eta_dept}
data = {
    y: y_train,
    s_ph: s_train,
    d_ph: d_train,
    dept_ph: dept_train,
    service_ph: service_train}
inference = ed.KLqp(latent_vars, data)

One way to critique the fitted model is a residual plot, i.e., a plot of the difference between the predicted value and the observed value for each data point. Below we manually run inference, initializing the algorithm and performing individual updates within a loop. We form residual plots as the algorithm progresses. This helps us examine how the algorithm proceeds to infer the random and fixed effects from data.

To form residuals, we first make predictions on test data. We do this by copying yhat defined in the model and replacing its dependence on random effects with their inferred means. During the algorithm, we evaluate the predictions, feeding in test inputs.

yhat_test = ed.copy(yhat, {
    eta_s: q_eta_s.mean(),
    eta_d: q_eta_d.mean(),
    eta_dept: q_eta_dept.mean()})

We now write the main loop.

inference.initialize(n_print=20, n_iter=100)

for _ in range(inference.n_iter):
  # Update and print progress of algorithm.
  info_dict = inference.update()

  t = info_dict['t']
  if t == 1 or t % inference.n_print == 0:
    # Make predictions on test data.
    yhat_vals = yhat_test.eval(feed_dict={
        s_ph: s_test,
        d_ph: d_test,
        dept_ph: dept_test,
        service_ph: service_test})

    # Form residual plot.
    plt.title("Residuals for Predicted Ratings on Test Set")
    plt.xlim(-4, 4)
    plt.ylim(0, 800)
    plt.hist(yhat_vals - y_test, 75)


Above, we described a method for diagnosing the fit of the model via residual plots. Below we show the residuals for the model post-convergence. (For intermediate residual plots, check the Jupyter notebook.)


The residuals appear normally distributed with mean 0. This provides evidence that the model correctly fits the data.


We thank Mayank Agrawal for writing the initial version of this tutorial.


Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48.

Gelman, A., & Hill, J. L. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.