我在玩这个代码，这是一元线性混合效应建模。数据集表示：学生作为s

讲师(d)

部门为部门

服务即服务

在R的lme4包语法中(Bates等人，2015)，实现的模型可以概括为：y ~ 1 + (1|students) + (1|instructor) + (1|dept) + service

其中1表示截距项，(1 | x)表示x的随机效应，x表示固定效应。在

^{pr2}$

s_train = train['s'].values

d_train = train['dcodes'].values

dept_train = train['deptcodes'].values

y_train = train['y'].values

service_train = train['service'].values

n_obs_train = train.shape[0]

s_test = test['s'].values

d_test = test['dcodes'].values

dept_test = test['deptcodes'].values

y_test = test['y'].values

service_test = test['service'].values

n_obs_test = test.shape[0]

n_s = max(s_train) + 1 # number of students

n_d = max(d_train) + 1 # number of instructors

n_dept = max(dept_train) + 1 # number of departments

n_obs = train.shape[0] # number of observations

# 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.get_variable("mu", [])

service = tf.get_variable("service", [])

sigma_s = tf.sqrt(tf.exp(tf.get_variable("sigma_s", [])))

sigma_d = tf.sqrt(tf.exp(tf.get_variable("sigma_d", [])))

sigma_dept = tf.sqrt(tf.exp(tf.get_variable("sigma_dept", [])))

# 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))

#Inference

q_eta_s = Normal(

loc=tf.get_variable("q_eta_s/loc", [n_s]),

scale=tf.nn.softplus(tf.get_variable("q_eta_s/scale", [n_s])))

q_eta_d = Normal(

loc=tf.get_variable("q_eta_d/loc", [n_d]),

scale=tf.nn.softplus(tf.get_variable("q_eta_d/scale", [n_d])))

q_eta_dept = Normal(

loc=tf.get_variable("q_eta_dept/loc", [n_dept]),

scale=tf.nn.softplus(tf.get_variable("q_eta_dept/scale", [n_dept])))

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)

这在线性混合效应建模的单变量情况下很好地工作。我试图把这种方法推广到多变量的情况。任何想法都是非常受欢迎的。在