hglm

  • Available in: GLM

  • Hyperparameter: no

Description

Generalized Linear Models (GLM) estimate regression models for outcomes following exponential distributions. 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). HGLM allows you to specify both fixed and random effects, which allows fitting correlated to random effects as well as random regression models.

HGLM produces estimates for fixed effects, random effects, variance components and their standard errors. It also produces diagnostics, such as variances and leverages.

The hglm option allows you to build a hierarchical generalized linear model. This option is disabled by default.

Note: This initial release of HGLM supports only the Gaussian family and random family.

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
m11H2O <- 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(m11H2O)

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 = "Device"

# 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)