rand_family

• Available in: GLM

• Hyperparameter: no

Description

Hierarchical GLM (HGLM) fits generalized linear models with random effects, where the random effect can come from a conjugate exponential-family distribution (for example, Gaussian). The rand_family option specifies the Random Family Component as an array to be used in GLM when HGLM=True.

Note: You must include one family for each random component. Only rand_family={"[gaussian"}) is currently supported.

Related Parameters

• HGLM

• random_columns

Example

R

Python

library(h2o)
h2o.init()

# Import the semiconductor dataset
h2odata <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/semiconductor.csv")

# Set the response, predictor, and random columns
yresp <- "y"
xlist <- c("x1", "x3", "x5", "x6")
z <- c(1)

# Convert the "Device" column to a factor
h2odata$Device <- h2o.asfactor(h2odata$Device)

# Train and view the model
h2o_glm <- h2o.glm(x = xlist,
                   y = yresp,
                   family = "gaussian",
                   rand_family = c("gaussian"),
                   rand_link = c("identity"),
                   training_frame = h2odata,
                   HGLM = TRUE,
                   random_columns = z,
                   calc_like = TRUE)
print(h2o_glm)

import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
h2o.init()

# Import the semiconductor dataset
h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/semiconductor.csv")

# Set the response, predictor, and random columns
y = "y"
x = ["x1","x3","x5","x6"]
z = [0]

# Convert the "Device" column to a factor
h2o_data["Device"] = h2o_data["Device"].asfactor()

# Train and view the model
h2o_glm = H2OGeneralizedLinearEstimator(HGLM=True,
                                        family="gaussian",
                                        rand_family=["gaussian"],
                                        random_columns=z,
                                        rand_link=["identity"],
                                        calc_like=True)
h2o_glm.train(x=x, y=y, training_frame=h2o_data)
print(h2o_glm)