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<table width="100%" summary="page for spark.glm {SparkR}"><tr><td>spark.glm {SparkR}</td><td align="right">R Documentation</td></tr></table>
<h2>Generalized Linear Models</h2>
<h3>Description</h3>
<p>Fits generalized linear model against a SparkDataFrame.
Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make
predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models.
</p>
<h3>Usage</h3>
<pre>
spark.glm(data, formula, ...)
## S4 method for signature 'SparkDataFrame,formula'
spark.glm(data, formula, family = gaussian,
tol = 1e-06, maxIter = 25, weightCol = NULL, regParam = 0)
## S4 method for signature 'GeneralizedLinearRegressionModel'
summary(object)
## S3 method for class 'summary.GeneralizedLinearRegressionModel'
print(x, ...)
## S4 method for signature 'GeneralizedLinearRegressionModel'
predict(object, newData)
## S4 method for signature 'GeneralizedLinearRegressionModel,character'
write.ml(object, path,
overwrite = FALSE)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>data</code></td>
<td>
<p>a SparkDataFrame for training.</p>
</td></tr>
<tr valign="top"><td><code>formula</code></td>
<td>
<p>a symbolic description of the model to be fitted. Currently only a few formula
operators are supported, including '~', '.', ':', '+', and '-'.</p>
</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
<p>additional arguments passed to the method.</p>
</td></tr>
<tr valign="top"><td><code>family</code></td>
<td>
<p>a description of the error distribution and link function to be used in the model.
This can be a character string naming a family function, a family function or
the result of a call to a family function. Refer R family at
<a href="https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html">https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html</a>.
Currently these families are supported: <code>binomial</code>, <code>gaussian</code>,
<code>Gamma</code>, and <code>poisson</code>.</p>
</td></tr>
<tr valign="top"><td><code>tol</code></td>
<td>
<p>positive convergence tolerance of iterations.</p>
</td></tr>
<tr valign="top"><td><code>maxIter</code></td>
<td>
<p>integer giving the maximal number of IRLS iterations.</p>
</td></tr>
<tr valign="top"><td><code>weightCol</code></td>
<td>
<p>the weight column name. If this is not set or <code>NULL</code>, we treat all instance
weights as 1.0.</p>
</td></tr>
<tr valign="top"><td><code>regParam</code></td>
<td>
<p>regularization parameter for L2 regularization.</p>
</td></tr>
<tr valign="top"><td><code>object</code></td>
<td>
<p>a fitted generalized linear model.</p>
</td></tr>
<tr valign="top"><td><code>x</code></td>
<td>
<p>summary object of fitted generalized linear model returned by <code>summary</code> function.</p>
</td></tr>
<tr valign="top"><td><code>newData</code></td>
<td>
<p>a SparkDataFrame for testing.</p>
</td></tr>
<tr valign="top"><td><code>path</code></td>
<td>
<p>the directory where the model is saved.</p>
</td></tr>
<tr valign="top"><td><code>overwrite</code></td>
<td>
<p>overwrites or not if the output path already exists. Default is FALSE
which means throw exception if the output path exists.</p>
</td></tr>
</table>
<h3>Value</h3>
<p><code>spark.glm</code> returns a fitted generalized linear model.
</p>
<p><code>summary</code> returns summary information of the fitted model, which is a list.
The list of components includes at least the <code>coefficients</code> (coefficients matrix, which includes
coefficients, standard error of coefficients, t value and p value),
<code>null.deviance</code> (null/residual degrees of freedom), <code>aic</code> (AIC)
and <code>iter</code> (number of iterations IRLS takes). If there are collinear columns in the data,
the coefficients matrix only provides coefficients.
</p>
<p><code>predict</code> returns a SparkDataFrame containing predicted labels in a column named
&quot;prediction&quot;.
</p>
<h3>Note</h3>
<p>spark.glm since 2.0.0
</p>
<p>summary(GeneralizedLinearRegressionModel) since 2.0.0
</p>
<p>print.summary.GeneralizedLinearRegressionModel since 2.0.0
</p>
<p>predict(GeneralizedLinearRegressionModel) since 1.5.0
</p>
<p>write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0
</p>
<h3>See Also</h3>
<p><a href="glm.html">glm</a>, <a href="read.ml.html">read.ml</a>
</p>
<h3>Examples</h3>
<pre><code class="r">## Not run:
##D sparkR.session()
##D data(iris)
##D df &lt;- createDataFrame(iris)
##D model &lt;- spark.glm(df, Sepal_Length ~ Sepal_Width, family = &quot;gaussian&quot;)
##D summary(model)
##D
##D # fitted values on training data
##D fitted &lt;- predict(model, df)
##D head(select(fitted, &quot;Sepal_Length&quot;, &quot;prediction&quot;))
##D
##D # save fitted model to input path
##D path &lt;- &quot;path/to/model&quot;
##D write.ml(model, path)
##D
##D # can also read back the saved model and print
##D savedModel &lt;- read.ml(path)
##D summary(savedModel)
## End(Not run)
</code></pre>
<hr><div align="center">[Package <em>SparkR</em> version 2.1.1 <a href="00Index.html">Index</a>]</div>
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