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| <table width="100%" summary="page for glm {SparkR}"><tr><td>glm {SparkR}</td><td align="right">R Documentation</td></tr></table> |
| |
| <h2>Fits a generalized linear model</h2> |
| |
| <h3>Description</h3> |
| |
| <p>Fits a generalized linear model, similarly to R's glm(). Also see the glmnet package. |
| </p> |
| |
| |
| <h3>Usage</h3> |
| |
| <pre> |
| glm(formula, family = gaussian, data, weights, subset, na.action, |
| start = NULL, etastart, mustart, offset, control = list(...), |
| model = TRUE, method = "glm.fit", x = FALSE, y = TRUE, |
| contrasts = NULL, ...) |
| |
| ## S4 method for signature 'formula,ANY,DataFrame' |
| glm(formula, family = c("gaussian", |
| "binomial"), data, lambda = 0, alpha = 0, standardize = TRUE, |
| solver = "auto") |
| </pre> |
| |
| |
| <h3>Arguments</h3> |
| |
| <table summary="R argblock"> |
| <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>family</code></td> |
| <td> |
| <p>Error distribution. "gaussian" -> linear regression, "binomial" -> logistic reg.</p> |
| </td></tr> |
| <tr valign="top"><td><code>data</code></td> |
| <td> |
| <p>DataFrame for training</p> |
| </td></tr> |
| <tr valign="top"><td><code>lambda</code></td> |
| <td> |
| <p>Regularization parameter</p> |
| </td></tr> |
| <tr valign="top"><td><code>alpha</code></td> |
| <td> |
| <p>Elastic-net mixing parameter (see glmnet's documentation for details)</p> |
| </td></tr> |
| <tr valign="top"><td><code>standardize</code></td> |
| <td> |
| <p>Whether to standardize features before training</p> |
| </td></tr> |
| <tr valign="top"><td><code>solver</code></td> |
| <td> |
| <p>The solver algorithm used for optimization, this can be "l-bfgs", "normal" and |
| "auto". "l-bfgs" denotes Limited-memory BFGS which is a limited-memory |
| quasi-Newton optimization method. "normal" denotes using Normal Equation as an |
| analytical solution to the linear regression problem. The default value is "auto" |
| which means that the solver algorithm is selected automatically.</p> |
| </td></tr> |
| </table> |
| |
| |
| <h3>Value</h3> |
| |
| <p>a fitted MLlib model |
| </p> |
| |
| |
| <h3>Examples</h3> |
| |
| <pre><code class="r">## Not run: |
| ##D sc <- sparkR.init() |
| ##D sqlContext <- sparkRSQL.init(sc) |
| ##D data(iris) |
| ##D df <- createDataFrame(sqlContext, iris) |
| ##D model <- glm(Sepal_Length ~ Sepal_Width, df, family="gaussian") |
| ##D summary(model) |
| ## End(Not run) |
| </code></pre> |
| |
| |
| <hr><div align="center">[Package <em>SparkR</em> version 1.6.3 <a href="00Index.html">Index</a>]</div> |
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