``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.

Related Paramters
~~~~~~~~~~~~~~~~~

none

Example
~~~~~~~

.. example-code::
   .. code-block:: r

    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)

   .. code-block:: python

    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)