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http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/spark.lda.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Latent Dirichlet Allocation</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark.lda {SparkR}"><tr><td>spark.lda {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Latent Dirichlet Allocation</h2>
+
+<h3>Description</h3>
+
+<p><code>spark.lda</code> fits a Latent Dirichlet Allocation model on a SparkDataFrame. Users can call
+<code>summary</code> to get a summary of the fitted LDA model, <code>spark.posterior</code> to compute
+posterior probabilities on new data, <code>spark.perplexity</code> to compute log perplexity on new
+data and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+spark.lda(data, ...)
+
+spark.posterior(object, newData)
+
+spark.perplexity(object, data)
+
+## S4 method for signature 'SparkDataFrame'
+spark.lda(data, features = "features", k = 10,
+ maxIter = 20, optimizer = c("online", "em"), subsamplingRate = 0.05,
+ topicConcentration = -1, docConcentration = -1,
+ customizedStopWords = "", maxVocabSize = bitwShiftL(1, 18))
+
+## S4 method for signature 'LDAModel'
+summary(object, maxTermsPerTopic)
+
+## S4 method for signature 'LDAModel,SparkDataFrame'
+spark.perplexity(object, data)
+
+## S4 method for signature 'LDAModel,SparkDataFrame'
+spark.posterior(object, newData)
+
+## S4 method for signature 'LDAModel,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>...</code></td>
+<td>
+<p>additional argument(s) passed to the method.</p>
+</td></tr>
+<tr valign="top"><td><code>object</code></td>
+<td>
+<p>A Latent Dirichlet Allocation model fitted by <code>spark.lda</code>.</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>features</code></td>
+<td>
+<p>Features column name. Either libSVM-format column or character-format column is
+valid.</p>
+</td></tr>
+<tr valign="top"><td><code>k</code></td>
+<td>
+<p>Number of topics.</p>
+</td></tr>
+<tr valign="top"><td><code>maxIter</code></td>
+<td>
+<p>Maximum iterations.</p>
+</td></tr>
+<tr valign="top"><td><code>optimizer</code></td>
+<td>
+<p>Optimizer to train an LDA model, "online" or "em", default is "online".</p>
+</td></tr>
+<tr valign="top"><td><code>subsamplingRate</code></td>
+<td>
+<p>(For online optimizer) Fraction of the corpus to be sampled and used in
+each iteration of mini-batch gradient descent, in range (0, 1].</p>
+</td></tr>
+<tr valign="top"><td><code>topicConcentration</code></td>
+<td>
+<p>concentration parameter (commonly named <code>beta</code> or <code>eta</code>) for
+the prior placed on topic distributions over terms, default -1 to set automatically on the
+Spark side. Use <code>summary</code> to retrieve the effective topicConcentration. Only 1-size
+numeric is accepted.</p>
+</td></tr>
+<tr valign="top"><td><code>docConcentration</code></td>
+<td>
+<p>concentration parameter (commonly named <code>alpha</code>) for the
+prior placed on documents distributions over topics (<code>theta</code>), default -1 to set
+automatically on the Spark side. Use <code>summary</code> to retrieve the effective
+docConcentration. Only 1-size or <code>k</code>-size numeric is accepted.</p>
+</td></tr>
+<tr valign="top"><td><code>customizedStopWords</code></td>
+<td>
+<p>stopwords that need to be removed from the given corpus. Ignore the
+parameter if libSVM-format column is used as the features column.</p>
+</td></tr>
+<tr valign="top"><td><code>maxVocabSize</code></td>
+<td>
+<p>maximum vocabulary size, default 1 << 18</p>
+</td></tr>
+<tr valign="top"><td><code>maxTermsPerTopic</code></td>
+<td>
+<p>Maximum number of terms to collect for each topic. Default value of 10.</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.lda</code> returns a fitted Latent Dirichlet Allocation model.
+</p>
+<p><code>summary</code> returns summary information of the fitted model, which is a list.
+The list includes
+</p>
+<table summary="R valueblock">
+<tr valign="top"><td><code><code>docConcentration</code></code></td>
+<td>
+<p>concentration parameter commonly named <code>alpha</code> for
+the prior placed on documents distributions over topics <code>theta</code></p>
+</td></tr>
+<tr valign="top"><td><code><code>topicConcentration</code></code></td>
+<td>
+<p>concentration parameter commonly named <code>beta</code> or
+<code>eta</code> for the prior placed on topic distributions over terms</p>
+</td></tr>
+<tr valign="top"><td><code><code>logLikelihood</code></code></td>
+<td>
+<p>log likelihood of the entire corpus</p>
+</td></tr>
+<tr valign="top"><td><code><code>logPerplexity</code></code></td>
+<td>
+<p>log perplexity</p>
+</td></tr>
+<tr valign="top"><td><code><code>isDistributed</code></code></td>
+<td>
+<p>TRUE for distributed model while FALSE for local model</p>
+</td></tr>
+<tr valign="top"><td><code><code>vocabSize</code></code></td>
+<td>
+<p>number of terms in the corpus</p>
+</td></tr>
+<tr valign="top"><td><code><code>topics</code></code></td>
+<td>
+<p>top 10 terms and their weights of all topics</p>
+</td></tr>
+<tr valign="top"><td><code><code>vocabulary</code></code></td>
+<td>
+<p>whole terms of the training corpus, NULL if libsvm format file
+used as training set</p>
+</td></tr>
+<tr valign="top"><td><code><code>trainingLogLikelihood</code></code></td>
+<td>
+<p>Log likelihood of the observed tokens in the training set,
+given the current parameter estimates:
+log P(docs | topics, topic distributions for docs, Dirichlet hyperparameters)
+It is only for distributed LDA model (i.e., optimizer = "em")</p>
+</td></tr>
+<tr valign="top"><td><code><code>logPrior</code></code></td>
+<td>
+<p>Log probability of the current parameter estimate:
+log P(topics, topic distributions for docs | Dirichlet hyperparameters)
+It is only for distributed LDA model (i.e., optimizer = "em")</p>
+</td></tr>
+</table>
+<p><code>spark.perplexity</code> returns the log perplexity of given SparkDataFrame, or the log
+perplexity of the training data if missing argument "data".
+</p>
+<p><code>spark.posterior</code> returns a SparkDataFrame containing posterior probabilities
+vectors named "topicDistribution".
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark.lda since 2.1.0
+</p>
+<p>summary(LDAModel) since 2.1.0
+</p>
+<p>spark.perplexity(LDAModel) since 2.1.0
+</p>
+<p>spark.posterior(LDAModel) since 2.1.0
+</p>
+<p>write.ml(LDAModel, character) since 2.1.0
+</p>
+
+
+<h3>See Also</h3>
+
+<p>topicmodels: <a href="https://cran.r-project.org/package=topicmodels">https://cran.r-project.org/package=topicmodels</a>
+</p>
+<p><a href="read.ml.html">read.ml</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D text <- read.df("data/mllib/sample_lda_libsvm_data.txt", source = "libsvm")
+##D model <- spark.lda(data = text, optimizer = "em")
+##D
+##D # get a summary of the model
+##D summary(model)
+##D
+##D # compute posterior probabilities
+##D posterior <- spark.posterior(model, text)
+##D showDF(posterior)
+##D
+##D # compute perplexity
+##D perplexity <- spark.perplexity(model, text)
+##D
+##D # save and load the model
+##D path <- "path/to/model"
+##D write.ml(model, path)
+##D savedModel <- read.ml(path)
+##D summary(savedModel)
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/spark.logit.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Logistic Regression Model</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark.logit {SparkR}"><tr><td>spark.logit {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Logistic Regression Model</h2>
+
+<h3>Description</h3>
+
+<p>Fits an logistic regression model against a SparkDataFrame. It supports "binomial": Binary logistic regression
+with pivoting; "multinomial": Multinomial logistic (softmax) regression without pivoting, similar to glmnet.
+Users can print, make predictions on the produced model and save the model to the input path.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+spark.logit(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.logit(data, formula, regParam = 0,
+ elasticNetParam = 0, maxIter = 100, tol = 1e-06, family = "auto",
+ standardization = TRUE, thresholds = 0.5, weightCol = NULL,
+ aggregationDepth = 2)
+
+## S4 method for signature 'LogisticRegressionModel'
+summary(object)
+
+## S4 method for signature 'LogisticRegressionModel'
+predict(object, newData)
+
+## S4 method for signature 'LogisticRegressionModel,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>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>regParam</code></td>
+<td>
+<p>the regularization parameter.</p>
+</td></tr>
+<tr valign="top"><td><code>elasticNetParam</code></td>
+<td>
+<p>the ElasticNet mixing parameter. For alpha = 0.0, the penalty is an L2 penalty.
+For alpha = 1.0, it is an L1 penalty. For 0.0 < alpha < 1.0, the penalty is a combination
+of L1 and L2. Default is 0.0 which is an L2 penalty.</p>
+</td></tr>
+<tr valign="top"><td><code>maxIter</code></td>
+<td>
+<p>maximum iteration number.</p>
+</td></tr>
+<tr valign="top"><td><code>tol</code></td>
+<td>
+<p>convergence tolerance of iterations.</p>
+</td></tr>
+<tr valign="top"><td><code>family</code></td>
+<td>
+<p>the name of family which is a description of the label distribution to be used in the model.
+Supported options:
+</p>
+
+<ul>
+<li><p>"auto": Automatically select the family based on the number of classes:
+If number of classes == 1 || number of classes == 2, set to "binomial".
+Else, set to "multinomial".
+</p>
+</li>
+<li><p>"binomial": Binary logistic regression with pivoting.
+</p>
+</li>
+<li><p>"multinomial": Multinomial logistic (softmax) regression without pivoting.
+</p>
+</li></ul>
+</td></tr>
+<tr valign="top"><td><code>standardization</code></td>
+<td>
+<p>whether to standardize the training features before fitting the model. The coefficients
+of models will be always returned on the original scale, so it will be transparent for
+users. Note that with/without standardization, the models should be always converged
+to the same solution when no regularization is applied. Default is TRUE, same as glmnet.</p>
+</td></tr>
+<tr valign="top"><td><code>thresholds</code></td>
+<td>
+<p>in binary classification, in range [0, 1]. If the estimated probability of class label 1
+is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0
+more often; a low threshold encourages the model to predict 1 more often. Note: Setting this with
+threshold p is equivalent to setting thresholds c(1-p, p). In multiclass (or binary) classification to adjust the probability of
+predicting each class. Array must have length equal to the number of classes, with values > 0,
+excepting that at most one value may be 0. The class with largest value p/t is predicted, where p
+is the original probability of that class and t is the class's threshold.</p>
+</td></tr>
+<tr valign="top"><td><code>weightCol</code></td>
+<td>
+<p>The weight column name.</p>
+</td></tr>
+<tr valign="top"><td><code>aggregationDepth</code></td>
+<td>
+<p>The depth for treeAggregate (greater than or equal to 2). If the dimensions of features
+or the number of partitions are large, this param could be adjusted to a larger size.
+This is an expert parameter. Default value should be good for most cases.</p>
+</td></tr>
+<tr valign="top"><td><code>object</code></td>
+<td>
+<p>an LogisticRegressionModel fitted by <code>spark.logit</code>.</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.logit</code> returns a fitted logistic regression model.
+</p>
+<p><code>summary</code> returns summary information of the fitted model, which is a list.
+The list includes <code>coefficients</code> (coefficients matrix of the fitted model).
+</p>
+<p><code>predict</code> returns the predicted values based on an LogisticRegressionModel.
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark.logit since 2.1.0
+</p>
+<p>summary(LogisticRegressionModel) since 2.1.0
+</p>
+<p>predict(LogisticRegressionModel) since 2.1.0
+</p>
+<p>write.ml(LogisticRegression, character) since 2.1.0
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session()
+##D # binary logistic regression
+##D t <- as.data.frame(Titanic)
+##D training <- createDataFrame(t)
+##D model <- spark.logit(training, Survived ~ ., regParam = 0.5)
+##D summary <- summary(model)
+##D
+##D # fitted values on training data
+##D fitted <- predict(model, training)
+##D
+##D # save fitted model to input path
+##D path <- "path/to/model"
+##D write.ml(model, path)
+##D
+##D # can also read back the saved model and predict
+##D # Note that summary deos not work on loaded model
+##D savedModel <- read.ml(path)
+##D summary(savedModel)
+##D
+##D # multinomial logistic regression
+##D
+##D model <- spark.logit(training, Class ~ ., regParam = 0.5)
+##D summary <- summary(model)
+##D
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/spark.mlp.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Multilayer Perceptron Classification Model</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark.mlp {SparkR}"><tr><td>spark.mlp {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Multilayer Perceptron Classification Model</h2>
+
+<h3>Description</h3>
+
+<p><code>spark.mlp</code> fits a multi-layer perceptron neural network 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.
+Only categorical data is supported.
+For more details, see
+<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html">
+Multilayer Perceptron</a>
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+spark.mlp(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.mlp(data, formula, layers,
+ blockSize = 128, solver = "l-bfgs", maxIter = 100, tol = 1e-06,
+ stepSize = 0.03, seed = NULL, initialWeights = NULL)
+
+## S4 method for signature 'MultilayerPerceptronClassificationModel'
+summary(object)
+
+## S4 method for signature 'MultilayerPerceptronClassificationModel'
+predict(object, newData)
+
+## S4 method for signature 'MultilayerPerceptronClassificationModel,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 <code>SparkDataFrame</code> of observations and labels for model fitting.</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>layers</code></td>
+<td>
+<p>integer vector containing the number of nodes for each layer.</p>
+</td></tr>
+<tr valign="top"><td><code>blockSize</code></td>
+<td>
+<p>blockSize parameter.</p>
+</td></tr>
+<tr valign="top"><td><code>solver</code></td>
+<td>
+<p>solver parameter, supported options: "gd" (minibatch gradient descent) or "l-bfgs".</p>
+</td></tr>
+<tr valign="top"><td><code>maxIter</code></td>
+<td>
+<p>maximum iteration number.</p>
+</td></tr>
+<tr valign="top"><td><code>tol</code></td>
+<td>
+<p>convergence tolerance of iterations.</p>
+</td></tr>
+<tr valign="top"><td><code>stepSize</code></td>
+<td>
+<p>stepSize parameter.</p>
+</td></tr>
+<tr valign="top"><td><code>seed</code></td>
+<td>
+<p>seed parameter for weights initialization.</p>
+</td></tr>
+<tr valign="top"><td><code>initialWeights</code></td>
+<td>
+<p>initialWeights parameter for weights initialization, it should be a
+numeric vector.</p>
+</td></tr>
+<tr valign="top"><td><code>object</code></td>
+<td>
+<p>a Multilayer Perceptron Classification Model fitted by <code>spark.mlp</code></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.mlp</code> returns a fitted Multilayer Perceptron Classification Model.
+</p>
+<p><code>summary</code> returns summary information of the fitted model, which is a list.
+The list includes <code>numOfInputs</code> (number of inputs), <code>numOfOutputs</code>
+(number of outputs), <code>layers</code> (array of layer sizes including input
+and output layers), and <code>weights</code> (the weights of layers).
+For <code>weights</code>, it is a numeric vector with length equal to the expected
+given the architecture (i.e., for 8-10-2 network, 112 connection weights).
+</p>
+<p><code>predict</code> returns a SparkDataFrame containing predicted labeled in a column named
+"prediction".
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark.mlp since 2.1.0
+</p>
+<p>summary(MultilayerPerceptronClassificationModel) since 2.1.0
+</p>
+<p>predict(MultilayerPerceptronClassificationModel) since 2.1.0
+</p>
+<p>write.ml(MultilayerPerceptronClassificationModel, character) since 2.1.0
+</p>
+
+
+<h3>See Also</h3>
+
+<p><a href="read.ml.html">read.ml</a>
+</p>
+<p><a href="write.ml.html">write.ml</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
+##D
+##D # fit a Multilayer Perceptron Classification Model
+##D model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 3), solver = "l-bfgs",
+##D maxIter = 100, tol = 0.5, stepSize = 1, seed = 1,
+##D initialWeights = c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
+##D
+##D # get the summary of the model
+##D summary(model)
+##D
+##D # make predictions
+##D predictions <- predict(model, df)
+##D
+##D # save and load the model
+##D path <- "path/to/model"
+##D write.ml(model, path)
+##D savedModel <- read.ml(path)
+##D summary(savedModel)
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Naive Bayes Models</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark.naiveBayes {SparkR}"><tr><td>spark.naiveBayes {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Naive Bayes Models</h2>
+
+<h3>Description</h3>
+
+<p><code>spark.naiveBayes</code> fits a Bernoulli naive Bayes 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.
+Only categorical data is supported.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+spark.naiveBayes(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.naiveBayes(data, formula,
+ smoothing = 1)
+
+## S4 method for signature 'NaiveBayesModel'
+summary(object)
+
+## S4 method for signature 'NaiveBayesModel'
+predict(object, newData)
+
+## S4 method for signature 'NaiveBayesModel,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 <code>SparkDataFrame</code> of observations and labels for model fitting.</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 argument(s) passed to the method. Currently only <code>smoothing</code>.</p>
+</td></tr>
+<tr valign="top"><td><code>smoothing</code></td>
+<td>
+<p>smoothing parameter.</p>
+</td></tr>
+<tr valign="top"><td><code>object</code></td>
+<td>
+<p>a naive Bayes model fitted by <code>spark.naiveBayes</code>.</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.naiveBayes</code> returns a fitted naive Bayes model.
+</p>
+<p><code>summary</code> returns summary information of the fitted model, which is a list.
+The list includes <code>apriori</code> (the label distribution) and
+<code>tables</code> (conditional probabilities given the target label).
+</p>
+<p><code>predict</code> returns a SparkDataFrame containing predicted labeled in a column named
+"prediction".
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark.naiveBayes since 2.0.0
+</p>
+<p>summary(NaiveBayesModel) since 2.0.0
+</p>
+<p>predict(NaiveBayesModel) since 2.0.0
+</p>
+<p>write.ml(NaiveBayesModel, character) since 2.0.0
+</p>
+
+
+<h3>See Also</h3>
+
+<p>e1071: <a href="https://cran.r-project.org/package=e1071">https://cran.r-project.org/package=e1071</a>
+</p>
+<p><a href="write.ml.html">write.ml</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D data <- as.data.frame(UCBAdmissions)
+##D df <- createDataFrame(data)
+##D
+##D # fit a Bernoulli naive Bayes model
+##D model <- spark.naiveBayes(df, Admit ~ Gender + Dept, smoothing = 0)
+##D
+##D # get the summary of the model
+##D summary(model)
+##D
+##D # make predictions
+##D predictions <- predict(model, df)
+##D
+##D # save and load the model
+##D path <- "path/to/model"
+##D write.ml(model, path)
+##D savedModel <- read.ml(path)
+##D summary(savedModel)
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Random Forest Model for Regression and Classification</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark.randomForest {SparkR}"><tr><td>spark.randomForest {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Random Forest Model for Regression and Classification</h2>
+
+<h3>Description</h3>
+
+<p><code>spark.randomForest</code> fits a Random Forest Regression model or Classification model on
+a SparkDataFrame. Users can call <code>summary</code> to get a summary of the fitted Random Forest
+model, <code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to
+save/load fitted models.
+For more details, see
+<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-regression">
+Random Forest Regression</a> and
+<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier">
+Random Forest Classification</a>
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+spark.randomForest(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.randomForest(data, formula,
+ type = c("regression", "classification"), maxDepth = 5, maxBins = 32,
+ numTrees = 20, impurity = NULL, featureSubsetStrategy = "auto",
+ seed = NULL, subsamplingRate = 1, minInstancesPerNode = 1,
+ minInfoGain = 0, checkpointInterval = 10, maxMemoryInMB = 256,
+ cacheNodeIds = FALSE)
+
+## S4 method for signature 'RandomForestRegressionModel'
+summary(object)
+
+## S3 method for class 'summary.RandomForestRegressionModel'
+print(x, ...)
+
+## S4 method for signature 'RandomForestClassificationModel'
+summary(object)
+
+## S3 method for class 'summary.RandomForestClassificationModel'
+print(x, ...)
+
+## S4 method for signature 'RandomForestRegressionModel'
+predict(object, newData)
+
+## S4 method for signature 'RandomForestClassificationModel'
+predict(object, newData)
+
+## S4 method for signature 'RandomForestRegressionModel,character'
+write.ml(object, path,
+ overwrite = FALSE)
+
+## S4 method for signature 'RandomForestClassificationModel,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>type</code></td>
+<td>
+<p>type of model, one of "regression" or "classification", to fit</p>
+</td></tr>
+<tr valign="top"><td><code>maxDepth</code></td>
+<td>
+<p>Maximum depth of the tree (>= 0).</p>
+</td></tr>
+<tr valign="top"><td><code>maxBins</code></td>
+<td>
+<p>Maximum number of bins used for discretizing continuous features and for choosing
+how to split on features at each node. More bins give higher granularity. Must be
+>= 2 and >= number of categories in any categorical feature.</p>
+</td></tr>
+<tr valign="top"><td><code>numTrees</code></td>
+<td>
+<p>Number of trees to train (>= 1).</p>
+</td></tr>
+<tr valign="top"><td><code>impurity</code></td>
+<td>
+<p>Criterion used for information gain calculation.
+For regression, must be "variance". For classification, must be one of
+"entropy" and "gini", default is "gini".</p>
+</td></tr>
+<tr valign="top"><td><code>featureSubsetStrategy</code></td>
+<td>
+<p>The number of features to consider for splits at each tree node.
+Supported options: "auto", "all", "onethird", "sqrt", "log2", (0.0-1.0], [1-n].</p>
+</td></tr>
+<tr valign="top"><td><code>seed</code></td>
+<td>
+<p>integer seed for random number generation.</p>
+</td></tr>
+<tr valign="top"><td><code>subsamplingRate</code></td>
+<td>
+<p>Fraction of the training data used for learning each decision tree, in
+range (0, 1].</p>
+</td></tr>
+<tr valign="top"><td><code>minInstancesPerNode</code></td>
+<td>
+<p>Minimum number of instances each child must have after split.</p>
+</td></tr>
+<tr valign="top"><td><code>minInfoGain</code></td>
+<td>
+<p>Minimum information gain for a split to be considered at a tree node.</p>
+</td></tr>
+<tr valign="top"><td><code>checkpointInterval</code></td>
+<td>
+<p>Param for set checkpoint interval (>= 1) or disable checkpoint (-1).</p>
+</td></tr>
+<tr valign="top"><td><code>maxMemoryInMB</code></td>
+<td>
+<p>Maximum memory in MB allocated to histogram aggregation.</p>
+</td></tr>
+<tr valign="top"><td><code>cacheNodeIds</code></td>
+<td>
+<p>If FALSE, the algorithm will pass trees to executors to match instances with
+nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching
+can speed up training of deeper trees. Users can set how often should the
+cache be checkpointed or disable it by setting checkpointInterval.</p>
+</td></tr>
+<tr valign="top"><td><code>object</code></td>
+<td>
+<p>A fitted Random Forest regression model or classification model.</p>
+</td></tr>
+<tr valign="top"><td><code>x</code></td>
+<td>
+<p>summary object of Random Forest regression model or classification model
+returned by <code>summary</code>.</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.randomForest</code> returns a fitted Random Forest model.
+</p>
+<p><code>summary</code> returns summary information of the fitted model, which is a list.
+The list of components includes <code>formula</code> (formula),
+<code>numFeatures</code> (number of features), <code>features</code> (list of features),
+<code>featureImportances</code> (feature importances), <code>maxDepth</code> (max depth of trees),
+<code>numTrees</code> (number of trees), and <code>treeWeights</code> (tree weights).
+</p>
+<p><code>predict</code> returns a SparkDataFrame containing predicted labeled in a column named
+"prediction".
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark.randomForest since 2.1.0
+</p>
+<p>summary(RandomForestRegressionModel) since 2.1.0
+</p>
+<p>print.summary.RandomForestRegressionModel since 2.1.0
+</p>
+<p>summary(RandomForestClassificationModel) since 2.1.0
+</p>
+<p>print.summary.RandomForestClassificationModel since 2.1.0
+</p>
+<p>predict(RandomForestRegressionModel) since 2.1.0
+</p>
+<p>predict(RandomForestClassificationModel) since 2.1.0
+</p>
+<p>write.ml(RandomForestRegressionModel, character) since 2.1.0
+</p>
+<p>write.ml(RandomForestClassificationModel, character) since 2.1.0
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D # fit a Random Forest Regression Model
+##D df <- createDataFrame(longley)
+##D model <- spark.randomForest(df, Employed ~ ., type = "regression", maxDepth = 5, maxBins = 16)
+##D
+##D # get the summary of the model
+##D summary(model)
+##D
+##D # make predictions
+##D predictions <- predict(model, df)
+##D
+##D # save and load the model
+##D path <- "path/to/model"
+##D write.ml(model, path)
+##D savedModel <- read.ml(path)
+##D summary(savedModel)
+##D
+##D # fit a Random Forest Classification Model
+##D t <- as.data.frame(Titanic)
+##D df <- createDataFrame(t)
+##D model <- spark.randomForest(df, Survived ~ Freq + Age, "classification")
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Accelerated Failure Time (AFT) Survival Regression Model</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark.survreg {SparkR}"><tr><td>spark.survreg {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Accelerated Failure Time (AFT) Survival Regression Model</h2>
+
+<h3>Description</h3>
+
+<p><code>spark.survreg</code> fits an accelerated failure time (AFT) survival regression model on
+a SparkDataFrame. Users can call <code>summary</code> to get a summary of the fitted AFT 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.survreg(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.survreg(data, formula,
+ aggregationDepth = 2)
+
+## S4 method for signature 'AFTSurvivalRegressionModel'
+summary(object)
+
+## S4 method for signature 'AFTSurvivalRegressionModel'
+predict(object, newData)
+
+## S4 method for signature 'AFTSurvivalRegressionModel,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 '-'.
+Note that operator '.' is not supported currently.</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>aggregationDepth</code></td>
+<td>
+<p>The depth for treeAggregate (greater than or equal to 2). If the dimensions of features
+or the number of partitions are large, this param could be adjusted to a larger size.
+This is an expert parameter. Default value should be good for most cases.</p>
+</td></tr>
+<tr valign="top"><td><code>object</code></td>
+<td>
+<p>a fitted AFT survival regression model.</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.survreg</code> returns a fitted AFT survival regression model.
+</p>
+<p><code>summary</code> returns summary information of the fitted model, which is a list.
+The list includes the model's <code>coefficients</code> (features, coefficients,
+intercept and log(scale)).
+</p>
+<p><code>predict</code> returns a SparkDataFrame containing predicted values
+on the original scale of the data (mean predicted value at scale = 1.0).
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark.survreg since 2.0.0
+</p>
+<p>summary(AFTSurvivalRegressionModel) since 2.0.0
+</p>
+<p>predict(AFTSurvivalRegressionModel) since 2.0.0
+</p>
+<p>write.ml(AFTSurvivalRegressionModel, character) since 2.0.0
+</p>
+
+
+<h3>See Also</h3>
+
+<p>survival: <a href="https://cran.r-project.org/package=survival">https://cran.r-project.org/package=survival</a>
+</p>
+<p><a href="write.ml.html">write.ml</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D df <- createDataFrame(ovarian)
+##D model <- spark.survreg(df, Surv(futime, fustat) ~ ecog_ps + rx)
+##D
+##D # get a summary of the model
+##D summary(model)
+##D
+##D # make predictions
+##D predicted <- predict(model, df)
+##D showDF(predicted)
+##D
+##D # save and load the model
+##D path <- "path/to/model"
+##D write.ml(model, path)
+##D savedModel <- read.ml(path)
+##D summary(savedModel)
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/spark.svmLinear.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Linear SVM Model</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark.svmLinear {SparkR}"><tr><td>spark.svmLinear {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Linear SVM Model</h2>
+
+<h3>Description</h3>
+
+<p>Fits a linear SVM model against a SparkDataFrame, similar to svm in e1071 package.
+Currently only supports binary classification model with linear kernel.
+Users can print, make predictions on the produced model and save the model to the input path.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+spark.svmLinear(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.svmLinear(data, formula,
+ regParam = 0, maxIter = 100, tol = 1e-06, standardization = TRUE,
+ threshold = 0, weightCol = NULL, aggregationDepth = 2)
+
+## S4 method for signature 'LinearSVCModel'
+predict(object, newData)
+
+## S4 method for signature 'LinearSVCModel'
+summary(object)
+
+## S4 method for signature 'LinearSVCModel,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>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>regParam</code></td>
+<td>
+<p>The regularization parameter. Only supports L2 regularization currently.</p>
+</td></tr>
+<tr valign="top"><td><code>maxIter</code></td>
+<td>
+<p>Maximum iteration number.</p>
+</td></tr>
+<tr valign="top"><td><code>tol</code></td>
+<td>
+<p>Convergence tolerance of iterations.</p>
+</td></tr>
+<tr valign="top"><td><code>standardization</code></td>
+<td>
+<p>Whether to standardize the training features before fitting the model. The coefficients
+of models will be always returned on the original scale, so it will be transparent for
+users. Note that with/without standardization, the models should be always converged
+to the same solution when no regularization is applied.</p>
+</td></tr>
+<tr valign="top"><td><code>threshold</code></td>
+<td>
+<p>The threshold in binary classification applied to the linear model prediction.
+This threshold can be any real number, where Inf will make all predictions 0.0
+and -Inf will make all predictions 1.0.</p>
+</td></tr>
+<tr valign="top"><td><code>weightCol</code></td>
+<td>
+<p>The weight column name.</p>
+</td></tr>
+<tr valign="top"><td><code>aggregationDepth</code></td>
+<td>
+<p>The depth for treeAggregate (greater than or equal to 2). If the dimensions of features
+or the number of partitions are large, this param could be adjusted to a larger size.
+This is an expert parameter. Default value should be good for most cases.</p>
+</td></tr>
+<tr valign="top"><td><code>object</code></td>
+<td>
+<p>a LinearSVCModel fitted by <code>spark.svmLinear</code>.</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.svmLinear</code> returns a fitted linear SVM model.
+</p>
+<p><code>predict</code> returns the predicted values based on a LinearSVCModel.
+</p>
+<p><code>summary</code> returns summary information of the fitted model, which is a list.
+The list includes <code>coefficients</code> (coefficients of the fitted model),
+<code>numClasses</code> (number of classes), <code>numFeatures</code> (number of features).
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark.svmLinear since 2.2.0
+</p>
+<p>predict(LinearSVCModel) since 2.2.0
+</p>
+<p>summary(LinearSVCModel) since 2.2.0
+</p>
+<p>write.ml(LogisticRegression, character) since 2.2.0
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session()
+##D t <- as.data.frame(Titanic)
+##D training <- createDataFrame(t)
+##D model <- spark.svmLinear(training, Survived ~ ., regParam = 0.5)
+##D summary <- summary(model)
+##D
+##D # fitted values on training data
+##D fitted <- predict(model, training)
+##D
+##D # save fitted model to input path
+##D path <- "path/to/model"
+##D write.ml(model, path)
+##D
+##D # can also read back the saved model and predict
+##D # Note that summary deos not work on loaded model
+##D savedModel <- read.ml(path)
+##D summary(savedModel)
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkR.callJMethod.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Call Java Methods</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.callJMethod {SparkR}"><tr><td>sparkR.callJMethod {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Call Java Methods</h2>
+
+<h3>Description</h3>
+
+<p>Call a Java method in the JVM running the Spark driver. The return
+values are automatically converted to R objects for simple objects. Other
+values are returned as "jobj" which are references to objects on JVM.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.callJMethod(x, methodName, ...)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>x</code></td>
+<td>
+<p>object to invoke the method on. Should be a "jobj" created by newJObject.</p>
+</td></tr>
+<tr valign="top"><td><code>methodName</code></td>
+<td>
+<p>method name to call.</p>
+</td></tr>
+<tr valign="top"><td><code>...</code></td>
+<td>
+<p>parameters to pass to the Java method.</p>
+</td></tr>
+</table>
+
+
+<h3>Details</h3>
+
+<p>This is a low level function to access the JVM directly and should only be used
+for advanced use cases. The arguments and return values that are primitive R
+types (like integer, numeric, character, lists) are automatically translated to/from
+Java types (like Integer, Double, String, Array). A full list can be found in
+serialize.R and deserialize.R in the Apache Spark code base.
+</p>
+
+
+<h3>Value</h3>
+
+<p>the return value of the Java method. Either returned as a R object
+if it can be deserialized or returned as a "jobj". See details section for more.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.callJMethod since 2.0.1
+</p>
+
+
+<h3>See Also</h3>
+
+<p><a href="sparkR.callJStatic.html">sparkR.callJStatic</a>, <a href="sparkR.newJObject.html">sparkR.newJObject</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session() # Need to have a Spark JVM running before calling newJObject
+##D # Create a Java ArrayList and populate it
+##D jarray <- sparkR.newJObject("java.util.ArrayList")
+##D sparkR.callJMethod(jarray, "add", 42L)
+##D sparkR.callJMethod(jarray, "get", 0L) # Will print 42
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkR.callJStatic.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Call Static Java Methods</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.callJStatic {SparkR}"><tr><td>sparkR.callJStatic {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Call Static Java Methods</h2>
+
+<h3>Description</h3>
+
+<p>Call a static method in the JVM running the Spark driver. The return
+value is automatically converted to R objects for simple objects. Other
+values are returned as "jobj" which are references to objects on JVM.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.callJStatic(x, methodName, ...)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>x</code></td>
+<td>
+<p>fully qualified Java class name that contains the static method to invoke.</p>
+</td></tr>
+<tr valign="top"><td><code>methodName</code></td>
+<td>
+<p>name of static method to invoke.</p>
+</td></tr>
+<tr valign="top"><td><code>...</code></td>
+<td>
+<p>parameters to pass to the Java method.</p>
+</td></tr>
+</table>
+
+
+<h3>Details</h3>
+
+<p>This is a low level function to access the JVM directly and should only be used
+for advanced use cases. The arguments and return values that are primitive R
+types (like integer, numeric, character, lists) are automatically translated to/from
+Java types (like Integer, Double, String, Array). A full list can be found in
+serialize.R and deserialize.R in the Apache Spark code base.
+</p>
+
+
+<h3>Value</h3>
+
+<p>the return value of the Java method. Either returned as a R object
+if it can be deserialized or returned as a "jobj". See details section for more.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.callJStatic since 2.0.1
+</p>
+
+
+<h3>See Also</h3>
+
+<p><a href="sparkR.callJMethod.html">sparkR.callJMethod</a>, <a href="sparkR.newJObject.html">sparkR.newJObject</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session() # Need to have a Spark JVM running before calling callJStatic
+##D sparkR.callJStatic("java.lang.System", "currentTimeMillis")
+##D sparkR.callJStatic("java.lang.System", "getProperty", "java.home")
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Get Runtime Config from the current active SparkSession</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.conf {SparkR}"><tr><td>sparkR.conf {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Get Runtime Config from the current active SparkSession</h2>
+
+<h3>Description</h3>
+
+<p>Get Runtime Config from the current active SparkSession.
+To change SparkSession Runtime Config, please see <code>sparkR.session()</code>.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.conf(key, defaultValue)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>key</code></td>
+<td>
+<p>(optional) The key of the config to get, if omitted, all config is returned</p>
+</td></tr>
+<tr valign="top"><td><code>defaultValue</code></td>
+<td>
+<p>(optional) The default value of the config to return if they config is not
+set, if omitted, the call fails if the config key is not set</p>
+</td></tr>
+</table>
+
+
+<h3>Value</h3>
+
+<p>a list of config values with keys as their names
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.conf since 2.0.0
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session()
+##D allConfigs <- sparkR.conf()
+##D masterValue <- unlist(sparkR.conf("spark.master"))
+##D namedConfig <- sparkR.conf("spark.executor.memory", "0g")
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkR.init-deprecated.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: (Deprecated) Initialize a new Spark Context</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.init {SparkR}"><tr><td>sparkR.init {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>(Deprecated) Initialize a new Spark Context</h2>
+
+<h3>Description</h3>
+
+<p>This function initializes a new SparkContext.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.init(master = "", appName = "SparkR",
+ sparkHome = Sys.getenv("SPARK_HOME"), sparkEnvir = list(),
+ sparkExecutorEnv = list(), sparkJars = "", sparkPackages = "")
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>master</code></td>
+<td>
+<p>The Spark master URL</p>
+</td></tr>
+<tr valign="top"><td><code>appName</code></td>
+<td>
+<p>Application name to register with cluster manager</p>
+</td></tr>
+<tr valign="top"><td><code>sparkHome</code></td>
+<td>
+<p>Spark Home directory</p>
+</td></tr>
+<tr valign="top"><td><code>sparkEnvir</code></td>
+<td>
+<p>Named list of environment variables to set on worker nodes</p>
+</td></tr>
+<tr valign="top"><td><code>sparkExecutorEnv</code></td>
+<td>
+<p>Named list of environment variables to be used when launching executors</p>
+</td></tr>
+<tr valign="top"><td><code>sparkJars</code></td>
+<td>
+<p>Character vector of jar files to pass to the worker nodes</p>
+</td></tr>
+<tr valign="top"><td><code>sparkPackages</code></td>
+<td>
+<p>Character vector of package coordinates</p>
+</td></tr>
+</table>
+
+
+<h3>Note</h3>
+
+<p>sparkR.init since 1.4.0
+</p>
+
+
+<h3>See Also</h3>
+
+<p><a href="sparkR.session.html">sparkR.session</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sc <- sparkR.init("local[2]", "SparkR", "/home/spark")
+##D sc <- sparkR.init("local[2]", "SparkR", "/home/spark",
+##D list(spark.executor.memory="1g"))
+##D sc <- sparkR.init("yarn-client", "SparkR", "/home/spark",
+##D list(spark.executor.memory="4g"),
+##D list(LD_LIBRARY_PATH="/directory of JVM libraries (libjvm.so) on workers/"),
+##D c("one.jar", "two.jar", "three.jar"),
+##D c("com.databricks:spark-avro_2.10:2.0.1"))
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Create Java Objects</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.newJObject {SparkR}"><tr><td>sparkR.newJObject {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Create Java Objects</h2>
+
+<h3>Description</h3>
+
+<p>Create a new Java object in the JVM running the Spark driver. The return
+value is automatically converted to an R object for simple objects. Other
+values are returned as a "jobj" which is a reference to an object on JVM.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.newJObject(x, ...)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>x</code></td>
+<td>
+<p>fully qualified Java class name.</p>
+</td></tr>
+<tr valign="top"><td><code>...</code></td>
+<td>
+<p>arguments to be passed to the constructor.</p>
+</td></tr>
+</table>
+
+
+<h3>Details</h3>
+
+<p>This is a low level function to access the JVM directly and should only be used
+for advanced use cases. The arguments and return values that are primitive R
+types (like integer, numeric, character, lists) are automatically translated to/from
+Java types (like Integer, Double, String, Array). A full list can be found in
+serialize.R and deserialize.R in the Apache Spark code base.
+</p>
+
+
+<h3>Value</h3>
+
+<p>the object created. Either returned as a R object
+if it can be deserialized or returned as a "jobj". See details section for more.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.newJObject since 2.0.1
+</p>
+
+
+<h3>See Also</h3>
+
+<p><a href="sparkR.callJMethod.html">sparkR.callJMethod</a>, <a href="sparkR.callJStatic.html">sparkR.callJStatic</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session() # Need to have a Spark JVM running before calling newJObject
+##D # Create a Java ArrayList and populate it
+##D jarray <- sparkR.newJObject("java.util.ArrayList")
+##D sparkR.callJMethod(jarray, "add", 42L)
+##D sparkR.callJMethod(jarray, "get", 0L) # Will print 42
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkR.session.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Get the existing SparkSession or initialize a new...</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.session {SparkR}"><tr><td>sparkR.session {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Get the existing SparkSession or initialize a new SparkSession.</h2>
+
+<h3>Description</h3>
+
+<p>SparkSession is the entry point into SparkR. <code>sparkR.session</code> gets the existing
+SparkSession or initializes a new SparkSession.
+Additional Spark properties can be set in <code>...</code>, and these named parameters take priority
+over values in <code>master</code>, <code>appName</code>, named lists of <code>sparkConfig</code>.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.session(master = "", appName = "SparkR",
+ sparkHome = Sys.getenv("SPARK_HOME"), sparkConfig = list(),
+ sparkJars = "", sparkPackages = "", enableHiveSupport = TRUE, ...)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>master</code></td>
+<td>
+<p>the Spark master URL.</p>
+</td></tr>
+<tr valign="top"><td><code>appName</code></td>
+<td>
+<p>application name to register with cluster manager.</p>
+</td></tr>
+<tr valign="top"><td><code>sparkHome</code></td>
+<td>
+<p>Spark Home directory.</p>
+</td></tr>
+<tr valign="top"><td><code>sparkConfig</code></td>
+<td>
+<p>named list of Spark configuration to set on worker nodes.</p>
+</td></tr>
+<tr valign="top"><td><code>sparkJars</code></td>
+<td>
+<p>character vector of jar files to pass to the worker nodes.</p>
+</td></tr>
+<tr valign="top"><td><code>sparkPackages</code></td>
+<td>
+<p>character vector of package coordinates</p>
+</td></tr>
+<tr valign="top"><td><code>enableHiveSupport</code></td>
+<td>
+<p>enable support for Hive, fallback if not built with Hive support; once
+set, this cannot be turned off on an existing session</p>
+</td></tr>
+<tr valign="top"><td><code>...</code></td>
+<td>
+<p>named Spark properties passed to the method.</p>
+</td></tr>
+</table>
+
+
+<h3>Details</h3>
+
+<p>When called in an interactive session, this method checks for the Spark installation, and, if not
+found, it will be downloaded and cached automatically. Alternatively, <code>install.spark</code> can
+be called manually.
+</p>
+<p>A default warehouse is created automatically in the current directory when a managed table is
+created via <code>sql</code> statement <code>CREATE TABLE</code>, for example. To change the location of the
+warehouse, set the named parameter <code>spark.sql.warehouse.dir</code> to the SparkSession. Along with
+the warehouse, an accompanied metastore may also be automatically created in the current
+directory when a new SparkSession is initialized with <code>enableHiveSupport</code> set to
+<code>TRUE</code>, which is the default. For more details, refer to Hive configuration at
+<a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables">http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables</a>.
+</p>
+<p>For details on how to initialize and use SparkR, refer to SparkR programming guide at
+<a href="http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession">http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession</a>.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.session since 2.0.0
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session()
+##D df <- read.json(path)
+##D
+##D sparkR.session("local[2]", "SparkR", "/home/spark")
+##D sparkR.session("yarn-client", "SparkR", "/home/spark",
+##D list(spark.executor.memory="4g"),
+##D c("one.jar", "two.jar", "three.jar"),
+##D c("com.databricks:spark-avro_2.10:2.0.1"))
+##D sparkR.session(spark.master = "yarn-client", spark.executor.memory = "4g")
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkR.session.stop.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Stop the Spark Session and Spark Context</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+</head><body>
+
+<table width="100%" summary="page for sparkR.session.stop {SparkR}"><tr><td>sparkR.session.stop {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Stop the Spark Session and Spark Context</h2>
+
+<h3>Description</h3>
+
+<p>Stop the Spark Session and Spark Context.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.session.stop()
+
+sparkR.stop()
+</pre>
+
+
+<h3>Details</h3>
+
+<p>Also terminates the backend this R session is connected to.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.session.stop since 2.0.0
+</p>
+<p>sparkR.stop since 1.4.0
+</p>
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkR.uiWebUrl.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Get the URL of the SparkUI instance for the current active...</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.uiWebUrl {SparkR}"><tr><td>sparkR.uiWebUrl {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Get the URL of the SparkUI instance for the current active SparkSession</h2>
+
+<h3>Description</h3>
+
+<p>Get the URL of the SparkUI instance for the current active SparkSession.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.uiWebUrl()
+</pre>
+
+
+<h3>Value</h3>
+
+<p>the SparkUI URL, or NA if it is disabled, or not started.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.uiWebUrl since 2.1.1
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session()
+##D url <- sparkR.uiWebUrl()
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Get version of Spark on which this application is running</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkR.version {SparkR}"><tr><td>sparkR.version {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Get version of Spark on which this application is running</h2>
+
+<h3>Description</h3>
+
+<p>Get version of Spark on which this application is running.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkR.version()
+</pre>
+
+
+<h3>Value</h3>
+
+<p>a character string of the Spark version
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkR.version since 2.0.1
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session()
+##D version <- sparkR.version()
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkRHive.init-deprecated.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: (Deprecated) Initialize a new HiveContext</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkRHive.init {SparkR}"><tr><td>sparkRHive.init {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>(Deprecated) Initialize a new HiveContext</h2>
+
+<h3>Description</h3>
+
+<p>This function creates a HiveContext from an existing JavaSparkContext
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkRHive.init(jsc = NULL)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>jsc</code></td>
+<td>
+<p>The existing JavaSparkContext created with SparkR.init()</p>
+</td></tr>
+</table>
+
+
+<h3>Details</h3>
+
+<p>Starting SparkR 2.0, a SparkSession is initialized and returned instead.
+This API is deprecated and kept for backward compatibility only.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkRHive.init since 1.4.0
+</p>
+
+
+<h3>See Also</h3>
+
+<p><a href="sparkR.session.html">sparkR.session</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sc <- sparkR.init()
+##D sqlContext <- sparkRHive.init(sc)
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/sparkRSQL.init-deprecated.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: (Deprecated) Initialize a new SQLContext</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sparkRSQL.init {SparkR}"><tr><td>sparkRSQL.init {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>(Deprecated) Initialize a new SQLContext</h2>
+
+<h3>Description</h3>
+
+<p>This function creates a SparkContext from an existing JavaSparkContext and
+then uses it to initialize a new SQLContext
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+sparkRSQL.init(jsc = NULL)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>jsc</code></td>
+<td>
+<p>The existing JavaSparkContext created with SparkR.init()</p>
+</td></tr>
+</table>
+
+
+<h3>Details</h3>
+
+<p>Starting SparkR 2.0, a SparkSession is initialized and returned instead.
+This API is deprecated and kept for backward compatibility only.
+</p>
+
+
+<h3>Note</h3>
+
+<p>sparkRSQL.init since 1.4.0
+</p>
+
+
+<h3>See Also</h3>
+
+<p><a href="sparkR.session.html">sparkR.session</a>
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sc <- sparkR.init()
+##D sqlContext <- sparkRSQL.init(sc)
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/f7ec1155/site/docs/2.2.0/api/R/spark_partition_id.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: Return the partition ID as a column</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for spark_partition_id {SparkR}"><tr><td>spark_partition_id {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>Return the partition ID as a column</h2>
+
+<h3>Description</h3>
+
+<p>Return the partition ID as a SparkDataFrame column.
+Note that this is nondeterministic because it depends on data partitioning and
+task scheduling.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+## S4 method for signature 'missing'
+spark_partition_id()
+
+spark_partition_id(x = "missing")
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>x</code></td>
+<td>
+<p>empty. Should be used with no argument.</p>
+</td></tr>
+</table>
+
+
+<h3>Details</h3>
+
+<p>This is equivalent to the SPARK_PARTITION_ID function in SQL.
+</p>
+
+
+<h3>Note</h3>
+
+<p>spark_partition_id since 2.0.0
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run: select(df, spark_partition_id())
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><title>R: SQL Query</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+<link rel="stylesheet" type="text/css" href="R.css">
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
+<script>hljs.initHighlightingOnLoad();</script>
+</head><body>
+
+<table width="100%" summary="page for sql {SparkR}"><tr><td>sql {SparkR}</td><td align="right">R Documentation</td></tr></table>
+
+<h2>SQL Query</h2>
+
+<h3>Description</h3>
+
+<p>Executes a SQL query using Spark, returning the result as a SparkDataFrame.
+</p>
+
+
+<h3>Usage</h3>
+
+<pre>
+## Default S3 method:
+sql(sqlQuery)
+</pre>
+
+
+<h3>Arguments</h3>
+
+<table summary="R argblock">
+<tr valign="top"><td><code>sqlQuery</code></td>
+<td>
+<p>A character vector containing the SQL query</p>
+</td></tr>
+</table>
+
+
+<h3>Value</h3>
+
+<p>SparkDataFrame
+</p>
+
+
+<h3>Note</h3>
+
+<p>sql since 1.4.0
+</p>
+
+
+<h3>Examples</h3>
+
+<pre><code class="r">## Not run:
+##D sparkR.session()
+##D path <- "path/to/file.json"
+##D df <- read.json(path)
+##D createOrReplaceTempView(df, "table")
+##D new_df <- sql("SELECT * FROM table")
+## End(Not run)
+</code></pre>
+
+
+<hr><div align="center">[Package <em>SparkR</em> version 2.2.0 <a href="00Index.html">Index</a>]</div>
+</body></html>
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